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ORCIDJoão Gama 0001
João Manuel Portela da Gama
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- affiliation: University of Porto, Laboratory of Artificial Intelligence and Decision Support (LIAAD), Portugal
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2020 – today
- 2025
[j139]Lala H. Rajaoarisoa
, Raubertin Randrianandraina, Grzegorz J. Nalepa, João Gama:
Decision-making systems improvement based on explainable artificial intelligence approaches for predictive maintenance. Eng. Appl. Artif. Intell. 139: 109601 (2025)- [j138]Luciano Caroprese, Francesco Sergio Pisani, Bruno Miguel Veloso, Matthias König, Giuseppe Manco, Holger H. Hoos, João Gama:
Modelling Concept Drift in Dynamic Data Streams for Recommender Systems. Trans. Recomm. Syst. 3(2): 24:1-24:28 (2025) - [c246]Aldo Marcelo Paim, João Gama, Bruno Veloso, Fabrício Enembreck, Rita P. Ribeiro:
Efficient Instance Selection in Tree-Based Models for Data Streams Classification. SAC 2025: 448-455 - [c245]Ruirui Zhao, Jianbin Sun, João Gama, Jiang Jiang:
Online Learning from Capricious Data streams with Flexible Hoeffding Tree. SAC 2025: 465-469 - [i41]Ehsan Aminian, João Gama, Rita P. Ribeiro:
Histogram approaches for imbalanced data streams regression. CoRR abs/2501.17568 (2025) - [i40]Matthias Jakobs, Bruno Veloso, João Gama:
Interpretable Rules for Online Failure Prediction: A Case Study on the Metro do Porto dataset. CoRR abs/2502.07394 (2025) - [i39]Afonso Lourenço, João Rodrigo, João Gama, Goreti Marreiros:
DFDT: Dynamic Fast Decision Tree for IoT Data Stream Mining on Edge Devices. CoRR abs/2502.14011 (2025) - [i38]Afonso Lourenço, João Gama, Eric P. Xing, Goreti Marreiros:
In-context learning of evolving data streams with tabular foundational models. CoRR abs/2502.16840 (2025) - [i37]Afonso Lourenço, João Rodrigo, João Gama, Goreti Marreiros:
On-device edge learning for IoT data streams: a survey. CoRR abs/2502.17788 (2025) - [i36]Pedro Reis, Ana Paula Serra, João Gama:
A Deep Learning Framework for Medium-Term Covariance Forecasting in Multi-Asset Portfolios. CoRR abs/2503.01581 (2025) - [i35]Pedro Reis, Ana Paula Serra, João Gama:
The Role of Deep Learning in Financial Asset Management: A Systematic Review. CoRR abs/2503.01591 (2025) - [i34]Chongsheng Zhang, Shuwen Wu, Yingqi Chen, Matthias Aßenmacher, Christian Heumann, Yi Men, Gaojuan Fan, João Gama:
OBD-Finder: Explainable Coarse-to-Fine Text-Centric Oracle Bone Duplicates Discovery. CoRR abs/2505.03836 (2025) - 2024
- [j137]Adrien Bécue, João Gama, Pedro Quelhas Brito:
AI's effect on innovation capacity in the context of industry 5.0: a scoping review. Artif. Intell. Rev. 57(8): 215 (2024) - [j136]Antonio R. Moya, Bruno Veloso, João Gama, Sebastián Ventura:
Improving hyper-parameter self-tuning for data streams by adapting an evolutionary approach. Data Min. Knowl. Discov. 38(3): 1289-1315 (2024) - [j135]Saulo Martiello Mastelini, Bruno Veloso, Max Halford, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
SWINN: Efficient nearest neighbor search in sliding windows using graphs. Inf. Fusion 101: 101979 (2024) - [j134]Douglas Castilho, Thársis T. P. Souza, Soong Moon Kang, João Gama, André C. P. L. F. de Carvalho:
Forecasting financial market structure from network features using machine learning. Knowl. Inf. Syst. 66(8): 4497-4525 (2024) - [j133]João Gama, Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Bruno Veloso:
From fault detection to anomaly explanation: A case study on predictive maintenance. J. Web Semant. 81: 100821 (2024) - [c244]Marcos Paulo Silva Gôlo, João Gama, Ricardo Marcondes Marcacini:
One-Class Learning for Data Stream Through Graph Neural Networks. BRACIS (4) 2024: 61-75 - [c243]César Andrade, Rita P. Ribeiro, João Gama:
Evaluating Short Text Stream Clustering on Large E-commerce Datasets. BRACIS (3) 2024: 245-259 - [c242]César Andrade, Rita P. Ribeiro, João Gama:
Community-Based Topic Modeling with Contextual Outlier Handling. CAEPIA 2024: 173-183 - [c241]Matías Molina, Bruno Veloso, Carlos Abreu Ferreira, Rita P. Ribeiro, João Gama:
More (Enough) Is Better: Towards Few-Shot Illegal Landfill Waste Segmentation. ECAI 2024: 4547-4554 - [c240]Paula Raissa Silva, João Vinagre, João Gama:
Federated Online Learning for Heavy Hitter Detection. ECAI 2024: 4689-4695 - [c239]Joana Santos, Nuno Silva, Carlos Ferreira, João Gama:
Online News Classification Using Large Language Models with Semantic Enrichment. EKAW (Companion) 2024 - [c238]Thiago Andrade, João Gama:
Next Location Prediction with Time-Evolving Markov Models over Data Streams. EPIA (3) 2024: 115-126 - [c237]Amelia Zafra, Bruno Veloso, João Gama:
Early Failure Detection for Air Production Unit in Metro Trains. HAIS (1) 2024: 339-351 - [c236]Bruna Alves, Ana Almeida, Catarina Silva, Daniela Pais, Rita P. Ribeiro, João Gama, José Maria Fernandes, Susana Brás, Raquel Sebastião:
Emotion-Enhanced Pain Assessment Protocol. HAR 2024: 385-400 - [c235]João Gama:
Recent Advances in Learning from Data Streams. IC3K 2024: 9 - [c234]Arijit Ukil, Angshul Majumdar, Antonio J. Jara, João Gama:
Deep Neural Network Model Compression and Signal Processing. ICASSP Workshops 2024: 179-183 - [c233]Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama:
S+t-SNE - Bringing Dimensionality Reduction to Data Streams. IDA (2) 2024: 95-106 - [c232]Matías Molina, Rita P. Ribeiro, Bruno Veloso, João Gama:
Super-Resolution Analysis for Landfill Waste Classification. IDA (1) 2024: 155-166 - [c231]Narjes Davari, Bruno Veloso, Rita Paula Ribeiro, João Manuel Portela da Gama:
Detecting and Explaining Anomalies in the Air Production Unit of a Train. SAC 2024: 358-364 - [c230]Thiago Andrade, João Gama:
Where Do We Go From Here? Location Prediction from Time-Evolving Markov Models. SAC 2024: 365-367 - [c229]Maciej Mozolewski, Szymon Bobek, Rita P. Ribeiro, Grzegorz J. Nalepa, João Gama:
Towards Evaluation of Explainable Artificial Intelligence in Streaming Data. xAI (4) 2024: 145-168 - [d4]Bruno Veloso, Carlos Martins, Raphael Espanha, Paula Raissa Silva, Raul Azevedo, João Gama:
Anonymised Phone Call Dataset for Anomaly Detection. Zenodo, 2024
- [i33]Teresa Salazar, João Gama, Helder Araújo, Pedro Henriques Abreu:
Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning. CoRR abs/2402.07586 (2024) - [i32]Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama:
S+t-SNE - Bringing dimensionality reduction to data streams. CoRR abs/2403.17643 (2024) - [i31]Matías Molina, Rita P. Ribeiro, Bruno Veloso, João Gama:
Super-Resolution Analysis for Landfill Waste Classification. CoRR abs/2404.01790 (2024) - [i30]João Gama, Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Bruno Veloso:
A Neuro-Symbolic Explainer for Rare Events: A Case Study on Predictive Maintenance. CoRR abs/2404.14455 (2024) - [i29]Sérgio M. Jesus, Pedro Saleiro, Inês Oliveira e Silva, Beatriz M. Jorge, Rita P. Ribeiro, João Gama, Pedro Bizarro, Rayid Ghani:
Aequitas Flow: Streamlining Fair ML Experimentation. CoRR abs/2405.05809 (2024) - [i28]Jakub Jakubowski, Natalia Wojak-Strzelecka, Rita P. Ribeiro, Sepideh Pashami, Szymon Bobek, João Gama, Grzegorz J. Nalepa:
Artificial Intelligence Approaches for Predictive Maintenance in the Steel Industry: A Survey. CoRR abs/2405.12785 (2024) - [i27]Chongsheng Zhang, George Almpanidis, Gaojuan Fan, Binquan Deng, Yanbo Zhang, Ji Liu, Aouaidjia Kamel, Paolo Soda, João Gama:
A Systematic Review on Long-Tailed Learning. CoRR abs/2408.00483 (2024) - 2023
- [j132]Joel D. Costa, Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Novelty detection for multi-label stream classification under extreme verification latency. Appl. Soft Comput. 141: 110265 (2023) - [j131]Shazia Tabassum, João Gama, Paulo J. Azevedo, Mário Cordeiro, Carlos Martins, Andre Martins:
Social network analytics and visualization: Dynamic topic-based influence analysis in evolving micro-blogs. Expert Syst. J. Knowl. Eng. 40(5) (2023) - [j130]Jorge Meira, Bruno Veloso, Verónica Bolón-Canedo, Goreti Marreiros, Amparo Alonso-Betanzos, João Gama:
Data-driven predictive maintenance framework for railway systems. Intell. Data Anal. 27(4): 1087-1102 (2023) - [j129]Sofia Fernandes, Hadi Fanaee-T, João Gama, Leo Tisljaric, Tomislav Smuc:
WINTENDED: WINdowed TENsor decomposition for Densification Event Detection in time-evolving networks. Mach. Learn. 112(2): 459-481 (2023) - [j128]Jie Lu, João Gama, Xin Yao, Leandro L. Minku:
Guest Editorial: Special Issue on Stream Learning. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6683-6685 (2023) - [j127]Paula Raissa Silva, João Vinagre, João Gama:
Towards federated learning: An overview of methods and applications. WIREs Data. Mining. Knowl. Discov. 13(2) (2023) - [c228]Arijit Ukil, João Gama, Antonio J. Jara, Leandro Marín:
Knowledge-driven Analytics and Systems Impacting Human Quality of Life- Neurosymbolic AI, Explainable AI and Beyond. CIKM 2023: 5296-5299 - [c227]Thiago Andrade, João Gama:
Which Way to Go - Finding Frequent Trajectories Through Clustering. DS 2023: 460-473 - [c226]Rafael M. Mamede, Nuno Paiva, João Gama:
Error Analysis on Industry Data: Using Weak Segment Detection for Local Model Agnostic Prediction Intervals. DS 2023: 661-672 - [c225]Thiago Andrade, Nirbhaya Shaji, Rita P. Ribeiro, João Gama:
Pollution Emission Patterns of Transportation in Porto, Portugal Through Network Analysis. EPIA (1) 2023: 215-226 - [c224]César Andrade, Rita P. Ribeiro, João Gama:
Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions. EPIA (1) 2023: 365-377 - [c223]Inês Martins, João S. Resende, João Gama:
Online Influence Forest for Streaming Anomaly Detection. IDA 2023: 274-286 - [c222]João Gama, Slawomir Nowaczyk, Sepideh Pashami, Rita P. Ribeiro, Grzegorz J. Nalepa, Bruno Veloso:
XAI for Predictive Maintenance. KDD 2023: 5798-5799 - [c221]Inês Oliveira e Silva, Rita P. Ribeiro, João Gama:
Anomaly Detection in Pet Behavioural Data. PKDD/ECML Workshops (2) 2023: 137-153 - [c220]Marta Barbosa, Carolina Ribeiro, Filipe Gomes, Rita P. Ribeiro, João Gama:
Data Science for Fighting Environmental Crime. PKDD/ECML Workshops (2) 2023: 181-196 - [c219]Sónia Teixeira, Ana Rita Nogueira, João Gama:
Fairness Analysis in Causal Models: An Application to Public Procurement. PKDD/ECML Workshops (2) 2023: 197-211 - [c218]Miguel E. P. Silva, Bruno Veloso, João Gama:
Predictive Maintenance, Adversarial Autoencoders and Explainability. ECML/PKDD (7) 2023: 260-275 - [c217]Paula Raissa Silva, João Vinagre, João Gama:
A DTW Approach for Complex Data A Case Study with Network Data Streams. SAC 2023: 402-409 - [c216]Thiago Andrade, João Gama:
Estimating Instantaneous Vehicle Emissions. SAC 2023: 422-424 - [c215]Szymon Bobek, Slawomir Nowaczyk, João Gama, Sepideh Pashami, Rita P. Ribeiro, Zahra Taghiyarrenani, Bruno Veloso, Lala H. Rajaoarisoa, Maciej Szelazek, Grzegorz J. Nalepa:
Why Industry 5.0 Needs XAI 2.0? xAI (Late-breaking Work, Demos, Doctoral Consortium) 2023: 1-6 - [e32]Albert Bifet, Ana Carolina Lorena, Rita P. Ribeiro, João Gama, Pedro H. Abreu:
Discovery Science - 26th International Conference, DS 2023, Porto, Portugal, October 9-11, 2023, Proceedings. Lecture Notes in Computer Science 14276, Springer 2023, ISBN 978-3-031-45274-1 [contents] - [e31]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Communications in Computer and Information Science 1752, Springer 2023, ISBN 978-3-031-23617-4 [contents] - [e30]Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita P. Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew W. Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana Miranda, Konstantinos Sechidis, Arif Canakoglu, Sara Pidò, Pietro Pinoli, Albert Bifet, Sepideh Pashami:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Communications in Computer and Information Science 1753, Springer 2023, ISBN 978-3-031-23632-7 [contents] - [d3]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
MetroPT-3 Dataset. UCI Machine Learning Repository, 2023 - [i26]Angelica Liguori, Luciano Caroprese, Marco Minici, Bruno Veloso, Francesco Spinnato, Mirco Nanni, Giuseppe Manco, João Gama:
Modeling Events and Interactions through Temporal Processes - A Survey. CoRR abs/2303.06067 (2023) - [i25]Longbing Cao, Hui Chen, Xuhui Fan, João Gama, Yew-Soon Ong, Vipin Kumar:
Bayesian Federated Learning: A Survey. CoRR abs/2304.13267 (2023) - [i24]Sepideh Pashami, Slawomir Nowaczyk, Yuantao Fan, Jakub Jakubowski, Nuno Paiva, Narjes Davari, Szymon Bobek, Samaneh Jamshidi, Hamid Sarmadi, Abdallah Alabdallah, Rita P. Ribeiro, Bruno Veloso, Moamar Sayed Mouchaweh, Lala H. Rajaoarisoa, Grzegorz J. Nalepa, João Gama:
Explainable Predictive Maintenance. CoRR abs/2306.05120 (2023) - 2022
- [j126]João Gama, Rita P. Ribeiro, Bruno Veloso:
Data-Driven Predictive Maintenance. IEEE Intell. Syst. 37(4): 27-29 (2022) - [j125]Inês Martins, João S. Resende, Patrícia R. Sousa, Simão Silva, Luis Antunes, João Gama:
Host-based IDS: A review and open issues of an anomaly detection system in IoT. Future Gener. Comput. Syst. 133: 95-113 (2022) - [j124]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Semi-causal decision trees. Prog. Artif. Intell. 11(1): 105-119 (2022) - [j123]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-Francois Marteau:
Open challenges for Machine Learning based Early Decision-Making research. SIGKDD Explor. 24(2): 12-31 (2022) - [j122]Ana Rita Nogueira, Andrea Pugnana, Salvatore Ruggieri, Dino Pedreschi, João Gama:
Methods and tools for causal discovery and causal inference. WIREs Data Mining Knowl. Discov. 12(2) (2022) - [c214]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Temporal Nodes Causal Discovery for in Intensive Care Unit Survival Analysis. EPIA 2022: 587-598 - [c213]Thiago Andrade, João Gama:
How are you Riding? Transportation Mode Identification from Raw GPS Data. EPIA 2022: 648-659 - [c212]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
An Exploratory Diagnosis of Artificial Intelligence Risks for a Responsible Governance. ICEGOV 2022: 25-31 - [c211]Ricardo Cerri, Elaine R. Faria, João Gama:
An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps. ICMLA 2022: 1071-1076 - [c210]Narjes Davari, Sepideh Pashami, Bruno Veloso, Slawomir Nowaczyk, Yuantao Fan, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Fault Detection Framework Based on LSTM Autoencoder: A Case Study for Volvo Bus Data Set. IDA 2022: 39-52 - [c209]Nirbhaya Shaji, João Gama, Rita P. Ribeiro, Pedro Gomes:
Bank Statements to Network Features: Extracting Features Out of Time Series Using Visibility Graph. IDA 2022: 278-289 - [c208]Sérgio M. Jesus, José Pombal, Duarte M. Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. NeurIPS 2022 - [c207]Rodrigo Salles, Jérôme Mendes, Rita P. Ribeiro, João Gama:
Fault Detection in Wastewater Treatment Plants: Application of Autoencoders Models with Streaming Data. PKDD/ECML Workshops (1) 2022: 55-70 - [c206]Sónia Teixeira, Bruno Veloso, José Coelho Rodrigues, João Gama:
Ethical and Technological AI Risks Classification: A Human Vs Machine Approach. PKDD/ECML Workshops (1) 2022: 150-166 - [c205]Nirbhaya Shaji, Thiago Andrade, Rita P. Ribeiro, João Gama:
Study on Correlation Between Vehicle Emissions and Air Quality in Porto. PKDD/ECML Workshops (1) 2022: 181-196 - [c204]Rita P. Ribeiro, Saulo Martiello Mastelini, Narjes Davari, Ehsan Aminian, Bruno Veloso, João Gama:
Online Anomaly Explanation: A Case Study on Predictive Maintenance. PKDD/ECML Workshops (2) 2022: 383-399 - [c203]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, João Gama:
Fault Forecasting Using Data-Driven Modeling: A Case Study for Metro do Porto Data Set. PKDD/ECML Workshops (2) 2022: 400-409 - [c202]Emanuel Sousa Tomé, Rita P. Ribeiro, Bruno Veloso, João Gama:
An Online Data-Driven Predictive Maintenance Approach for Railway Switches. PKDD/ECML Workshops (2) 2022: 410-422 - [e29]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13280, Springer 2022, ISBN 978-3-031-05932-2 [contents] - [e28]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13281, Springer 2022, ISBN 978-3-031-05935-3 [contents] - [e27]João Gama, Tianrui Li, Yang Yu, Enhong Chen, Yu Zheng, Fei Teng:
Advances in Knowledge Discovery and Data Mining - 26th Pacific-Asia Conference, PAKDD 2022, Chengdu, China, May 16-19, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13282, Springer 2022, ISBN 978-3-031-05980-3 [contents] - [d2]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT: A Benchmark dataset for predictive maintenance. Version 1. Zenodo, 2022 [all versions] - [d1]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Pereira:
MetroPT2: A Benchmark dataset for predictive maintenance. Version V2. Zenodo, 2022 [all versions] - [i23]Alexis Bondu, Youssef Achenchabe, Albert Bifet, Fabrice Clérot, Antoine Cornuéjols, João Gama, Georges Hébrail, Vincent Lemaire, Pierre-François Marteau:
Open challenges for Machine Learning based Early Decision-Making research. CoRR abs/2204.13111 (2022) - [i22]Paula Raissa Silva, João Vinagre, João Gama:
Federated Anomaly Detection over Distributed Data Streams. CoRR abs/2205.07829 (2022) - [i21]Rui Portocarrero Sarmento, Douglas de O. Cardoso, João Gama, Pavel Brazdil:
Contextualization for the Organization of Text Documents Streams. CoRR abs/2206.02632 (2022) - [i20]Bruno Veloso, João Gama, Rita P. Ribeiro, Pedro Mota Pereira:
A Benchmark dataset for predictive maintenance. CoRR abs/2207.05466 (2022) - [i19]Sérgio M. Jesus, José Pombal, Duarte M. Alves, André Ferreira Cruz, Pedro Saleiro, Rita P. Ribeiro, João Gama, Pedro Bizarro:
Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation. CoRR abs/2211.13358 (2022) - [i18]Sónia Teixeira, José Rodrigues, Bruno Veloso, João Gama:
Humans Versus Machines: The Perspective of Two Different Approaches in Classification for Ethical Design. ERCIM News 2022(131) (2022) - 2021
- [j121]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Tensor decomposition for analysing time-evolving social networks: an overview. Artif. Intell. Rev. 54(4): 2891-2916 (2021) - [j120]Adrien Bécue, Isabel Praça, João Gama:
Artificial intelligence, cyber-threats and Industry 4.0: challenges and opportunities. Artif. Intell. Rev. 54(5): 3849-3886 (2021) - [j119]Ehsan Aminian, Rita P. Ribeiro, João Gama:
Chebyshev approaches for imbalanced data streams regression models. Data Min. Knowl. Discov. 35(6): 2389-2466 (2021) - [j118]Paulo Paraíso, Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Using network features for credit scoring in microfinance. Int. J. Data Sci. Anal. 12(2): 121-134 (2021) - [j117]Bruno Veloso, João Gama, Benedita Malheiro, João Vinagre:
Hyperparameter self-tuning for data streams. Inf. Fusion 76: 75-86 (2021) - [j116]Roberto Corizzo, Michelangelo Ceci, Hadi Fanaee-T, João Gama:
Multi-aspect renewable energy forecasting. Inf. Sci. 546: 701-722 (2021) - [j115]Narjes Davari, Bruno Veloso, Gustavo de Assis Costa, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
A Survey on Data-Driven Predictive Maintenance for the Railway Industry. Sensors 21(17): 5739 (2021) - [j114]Rui Portocarrero Sarmento, Douglas de O. Cardoso, Kemmily Dearo, Pavel Brazdil, João Gama:
Text documents streams with improved incremental similarity. Soc. Netw. Anal. Min. 11(1): 113 (2021) - [j113]João Vinagre, Alípio Mário Jorge, Conceição Rocha, João Gama:
Statistically Robust Evaluation of Stream-Based Recommender Systems. IEEE Trans. Knowl. Data Eng. 33(7): 2971-2982 (2021) - [j112]Maroua Bahri, Albert Bifet, João Gama, Heitor Murilo Gomes, Silviu Maniu:
Data stream analysis: Foundations, major tasks and tools. WIREs Data Mining Knowl. Discov. 11(3) (2021) - [c201]João Gama, Bruno Veloso, Ehsan Aminian, Rita P. Ribeiro:
Current Trends in Learning from Data Streams. BDA 2021: 183-193 - [c200]Jean-Gabriel Gaudreault, Paula Branco, João Gama:
An Analysis of Performance Metrics for Imbalanced Classification. DS 2021: 67-77 - [c199]Narjes Davari, Bruno Veloso, Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Predictive maintenance based on anomaly detection using deep learning for air production unit in the railway industry. DSAA 2021: 1-10 - [c198]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama, Alberto A. Pinto:
Generalised Partial Association in Causal Rules Discovery. EPIA 2021: 485-497 - [c197]Shazia Tabassum, João Gama, Paulo Azevedo, Luis Teixeira, Carlos Martins, Andre Martins:
Dynamic Topic Modeling Using Social Network Analytics. EPIA 2021: 498-509 - [c196]Patrício Costa, Ana Rita Nogueira, João Gama:
Modelling Voting Behaviour During a General Election Campaign Using Dynamic Bayesian Networks. EPIA 2021: 524-536 - [c195]Sérgio M. Jesus, Catarina G. Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer?: An Application-grounded Evaluation of Post-hoc Explanations. FAccT 2021: 805-815 - [c194]Bruno Veloso, Luciano Caroprese, Matthias König, Sónia Teixeira, Giuseppe Manco, Holger H. Hoos, João Gama:
Hyper-parameter Optimization for Latent Spaces. ECML/PKDD (3) 2021: 249-264 - [c193]Sónia Teixeira, Guilherme Londres, Bruno Veloso, Rita P. Ribeiro, João Gama:
Improving Smart Waste Collection Using AutoML. PKDD/ECML Workshops (2) 2021: 283-298 - [c192]Ricardo Cerri, Joel David Costa Júnior, Elaine R. Faria, João Gama:
A new self-organizing map based algorithm for multi-label stream classification. SAC 2021: 418-426 - [e26]Pedro Henriques Abreu, Pedro Pereira Rodrigues, Alberto Fernández, João Gama:
Advances in Intelligent Data Analysis XIX - 19th International Symposium on Intelligent Data Analysis, IDA 2021, Porto, Portugal, April 26-28, 2021, Proceedings. Lecture Notes in Computer Science 12695, Springer 2021, ISBN 978-3-030-74250-8 [contents] - [e25]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part I. Communications in Computer and Information Science 1524, Springer 2021, ISBN 978-3-030-93735-5 [contents] - [e24]Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher L. Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastián Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita P. Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça:
Machine Learning and Principles and Practice of Knowledge Discovery in Databases - International Workshops of ECML PKDD 2021, Virtual Event, September 13-17, 2021, Proceedings, Part II. Communications in Computer and Information Science 1525, Springer 2021, ISBN 978-3-030-93732-4 [contents] - [i17]Sérgio M. Jesus, Catarina G. Belém, Vladimir Balayan, João Bento, Pedro Saleiro, Pedro Bizarro, João Gama:
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations. CoRR abs/2101.08758 (2021) - [i16]Douglas Castilho, Thársis Tuani Pinto Souza, Soong Moon Kang, João Gama, André C. P. L. F. de Carvalho:
Forecasting Financial Market Structure from Network Features using Machine Learning. CoRR abs/2110.11751 (2021) - 2020
- [j111]Albert Bifet, João Gama:
IoT data stream analytics. Ann. des Télécommunications 75(9-10): 491-492 (2020) - [j110]Thiago Andrade, Brais Cancela, João Gama:
Discovering locations and habits from human mobility data. Ann. des Télécommunications 75(9-10): 505-521 (2020) - [j109]Shazia Tabassum, Muhammad Ajmal Azad, João Gama:
Profiling high leverage points for detecting anomalous users in telecom data networks. Ann. des Télécommunications 75(9-10): 573-581 (2020) - [j108]Bruno Veloso, Shazia Tabassum, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Interconnect bypass fraud detection: a case study. Ann. des Télécommunications 75(9-10): 583-596 (2020) - [j107]Felipe Azevedo Pinage, Eulanda Miranda dos Santos, João Gama:
A drift detection method based on dynamic classifier selection. Data Min. Knowl. Discov. 34(1): 50-74 (2020) - [j106]Sofia Fernandes, Hadi Fanaee-T, João Gama:
NORMO: A new method for estimating the number of components in CP tensor decomposition. Eng. Appl. Artif. Intell. 96: 103926 (2020) - [j105]Thiago Andrade, Brais Cancela, João Gama:
From mobility data to habits and common pathways. Expert Syst. J. Knowl. Eng. 37(6) (2020) - [j104]Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama:
A scalable saliency-based feature selection method with instance-level information. Knowl. Based Syst. 192: 105326 (2020) - [j103]Shazia Tabassum, Bruno Veloso, João Gama:
On fast and scalable recurring link's prediction in evolving multi-graph streams. Netw. Sci. 8(S1): S65-S81 (2020) - [j102]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks. IEEE Trans. Knowl. Data Eng. 32(2): 234-245 (2020) - [c191]Maroua Bahri, Bruno Veloso, Albert Bifet, João Gama:
AutoML for Stream k-Nearest Neighbors Classification. IEEE BigData 2020: 597-602 - [c190]Teru Fujii, Masahito Kumano, João Gama, Masahiro Kimura:
Detecting Geographical Competitive Structure for POI Visit Dynamics. COMPLEX NETWORKS (2) 2020: 27-38 - [c189]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Assembled Feature Selection for Credit Scoring in Microfinance with Non-traditional Features. DS 2020: 207-216 - [c188]Leo Tisljaric, Sofia Fernandes, Tonci Caric, João Gama:
Spatiotemporal Traffic Anomaly Detection on Urban Road Network Using Tensor Decomposition Method. DS 2020: 674-688 - [c187]Paulo Paraíso, Pedro Gomes, Saulo Ruiz, Luís Rodrigues, João Gama:
Using Network Features for Credit Scoring in MicroFinance: Extended Abstract. DSAA 2020: 783-784 - [c186]Ana Rita Nogueira, João Gama, Carlos Abreu Ferreira:
Improving Prediction with Causal Probabilistic Variables. IDA 2020: 379-390 - [c185]Bruno Veloso, Benedita Malheiro, Juan-Carlos Burguillo, João Gama:
Impact of Trust and Reputation Based Brokerage on the CloudAnchor Platform. PAAMS 2020: 303-314 - [c184]Bruno Veloso, João Gama:
Self Hyper-parameter Tuning for Stream Classification Algorithms. IoT Streams/ITEM@PKDD/ECML 2020: 3-13 - [c183]Mariana Barros, Bruno Veloso, Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Detection of an Air Production Unit in Operational Context. IoT Streams/ITEM@PKDD/ECML 2020: 61-74 - [c182]Bruno Veloso, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Fraud detection using heavy hitters: a case study. SAC 2020: 482-489 - [e23]Albert Bifet, Michele Berlingerio, João Gama, Jesse Read, Ana Rita Nogueira:
Proceedings of the 8th International Workshop on Big Data, IoT Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications co-located with 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2019), Anchorage, Alaska, August 4-8, 2019. CEUR Workshop Proceedings 2579, CEUR-WS.org 2020 [contents] - [e22]Irena Koprinska, Michael Kamp, Annalisa Appice, Corrado Loglisci, Luiza Antonie, Albrecht Zimmermann, Riccardo Guidotti, Özlem Özgöbek, Rita P. Ribeiro, Ricard Gavaldà, João Gama, Linara Adilova, Yamuna Krishnamurthy, Pedro M. Ferreira, Donato Malerba, Ibéria Medeiros, Michelangelo Ceci, Giuseppe Manco, Elio Masciari, Zbigniew W. Ras, Peter Christen, Eirini Ntoutsi, Erich Schubert, Arthur Zimek, Anna Monreale, Przemyslaw Biecek, Salvatore Rinzivillo, Benjamin Kille, Andreas Lommatzsch, Jon Atle Gulla:
ECML PKDD 2020 Workshops - Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020): SoGood 2020, PDFL 2020, MLCS 2020, NFMCP 2020, DINA 2020, EDML 2020, XKDD 2020 and INRA 2020, Ghent, Belgium, September 14-18, 2020, Proceedings. Communications in Computer and Information Science 1323, Springer 2020, ISBN 978-3-030-65964-6 [contents] - [e21]João Gama, Sepideh Pashami, Albert Bifet, Moamar Sayed Mouchaweh, Holger Fröning, Franz Pernkopf, Gregor Schiele, Michaela Blott:
IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning - Second International Workshop, IoT Streams 2020, and First International Workshop, ITEM 2020, Co-located with ECML/PKDD 2020, Ghent, Belgium, September 14-18, 2020, Revised Selected Papers. Communications in Computer and Information Science 1325, Springer 2020, ISBN 978-3-030-66769-6 [contents] - [i15]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. CoRR abs/2004.05785 (2020) - [i14]Ricardo Cerri, Joel David Costa Júnior, Elaine Ribeiro de Faria Paiva, João Manuel Portela da Gama:
Multi-label Stream Classification with Self-Organizing Maps. CoRR abs/2004.09397 (2020) - [i13]Sónia Teixeira, João Gama, Pedro Amorim, Gonçalo Figueira:
Trustability in Algorithmic Systems Based on Artificial Intelligence in the Public and Private Sectors. ERCIM News 2020(122) (2020)
2010 – 2019
- 2019
- [j101]Guoliang Li, João Gama, Jun Yang:
Special Issue of DASFAA 2019. Data Sci. Eng. 4(3): 177-178 (2019) - [j100]Eduarda Portela, Rita P. Ribeiro, João Gama:
The search of conditional outliers. Intell. Data Anal. 23(1): 23-39 (2019) - [j99]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Credit scoring for microfinance using behavioral data in emerging markets. Intell. Data Anal. 23(6): 1355-1378 (2019) - [j98]Fabíola Souza Fernandes Pereira, Claudio D. G. Linhares, Jean R. Ponciano, João Gama, Sandra de Amo, Gina M. B. Oliveira:
Uma Análise sobre a Evolução das Preferências Musicais dos Usuários Utilizando Redes de Similaridade Temporal. Braz. J. Inf. Syst. 12(3): 94-115 (2019) - [j97]Jakub Zgraja, Richard Hugh Moulton, João Gama, Andrzej Kasprzak, Michal Wozniak:
Adapting ClusTree for more challenging data stream environments. J. Intell. Fuzzy Syst. 37(6): 7679-7688 (2019) - [j96]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Contrasting logical sequences in multi-relational learning. Prog. Artif. Intell. 8(4): 487-503 (2019) - [j95]Wesllen Sousa Lima, Eduardo Souto, Khalil El-Khatib, Roozbeh Jalali, João Gama:
Human Activity Recognition Using Inertial Sensors in a Smartphone: An Overview. Sensors 19(14): 3213 (2019) - [j94]Heitor Murilo Gomes, Jesse Read, Albert Bifet, Jean Paul Barddal, João Gama:
Machine learning for streaming data: state of the art, challenges, and opportunities. SIGKDD Explor. 21(2): 6-22 (2019) - [j93]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 31(12): 2346-2363 (2019) - [c181]Joel D. Costa Júnior, Elaine Ribeiro de Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Novelty Detection for Multi-Label Stream Classification. BRACIS 2019: 144-149 - [c180]Mário Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil, Masahiro Kimura, João Gama:
Identifying, Ranking and Tracking Community Leaders in Evolving Social Networks. COMPLEX NETWORKS (1) 2019: 198-210 - [c179]Andreia Conceição, João Gama:
Main Factors Driving the Open Rate of Email Marketing Campaigns. DS 2019: 145-154 - [c178]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Evolving Social Networks Analysis via Tensor Decompositions: From Global Event Detection Towards Local Pattern Discovery and Specification. DS 2019: 385-395 - [c177]Thiago Andrade, Brais Cancela, João Gama:
Discovering Common Pathways Across Users' Habits in Mobility Data. EPIA (2) 2019: 410-421 - [c176]Douglas Castilho, João Gama, Leandro Resende Mundim, André C. P. L. F. de Carvalho:
Improving Portfolio Optimization Using Weighted Link Prediction in Dynamic Stock Networks. ICCS (3) 2019: 340-353 - [c175]Amal Saadallah, Luís Moreira-Matias, Ricardo Teixeira Sousa, Jihed Khiari, Erik Jenelius, João Gama:
BRIGHT - Drift-Aware Demand Predictions for Taxi Networks (Extended Abstract). ICDE 2019: 2145-2146 - [c174]Joel D. Costa Júnior, Elaine R. Faria, Jonathan de Andrade Silva, João Gama, Ricardo Cerri:
Pruned Sets for Multi-Label Stream Classification without True Labels. IJCNN 2019: 1-8 - [c173]Bruno Veloso, Carlos Martins, Raphael Espanha, Raul Azevedo, João Gama:
Detecting Bursts of Activity in Telecommunications. BigMine@KDD 2019 - [c172]Ehsan Aminian, Rita P. Ribeiro, João Gama:
A Study on Imbalanced Data Streams. PKDD/ECML Workshops (2) 2019: 380-389 - [c171]Thiago Andrade, Brais Cancela, João Gama:
Mining Human Mobility Data to Discover Locations and Habits. PKDD/ECML Workshops (2) 2019: 390-401 - [c170]Guilherme Londres, Nuno Filipe, João Gama:
Optimizing Waste Collection: A Data Mining Approach. PKDD/ECML Workshops (1) 2019: 570-578 - [e20]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11446, Springer 2019, ISBN 978-3-030-18575-6 [contents] - [e19]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11447, Springer 2019, ISBN 978-3-030-18578-7 [contents] - [e18]Guoliang Li, Jun Yang, João Gama, Juggapong Natwichai, Yongxin Tong:
Database Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Chiang Mai, Thailand, April 22-25, 2019, Proceedings, Part III, and DASFAA 2019 International Workshops: BDMS, BDQM, and GDMA, Chiang Mai, Thailand, April 22-25, 2019, Proceedings. Lecture Notes in Computer Science 11448, Springer 2019, ISBN 978-3-030-18589-3 [contents] - [e17]Anna Monreale, Carlos Alzate, Michael Kamp, Yamuna Krishnamurthy, Daniel Paurat, Moamar Sayed Mouchaweh, Albert Bifet, João Gama, Rita P. Ribeiro:
ECML PKDD 2018 Workshops - DMLE 2018 and IoTStream 2018, Dublin, Ireland, September 10-14, 2018, Revised Selected Papers. Communications in Computer and Information Science 967, Springer 2019, ISBN 978-3-030-14879-9 [contents] - [i12]Thiago Andrade, João Gama:
Identifying Points of Interest and Similar Individuals from Raw GPS Data. CoRR abs/1904.09357 (2019) - [i11]Brais Cancela, Verónica Bolón-Canedo, Amparo Alonso-Betanzos, João Gama:
A scalable saliency-based Feature selection method with instance level information. CoRR abs/1904.13127 (2019) - [i10]Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama:
Contextual One-Class Classification in Data Streams. CoRR abs/1907.04233 (2019) - [i9]Thiago Andrade, Brais Cancela, João Gama:
Mining Human Mobility Data to Discover Locations and Habits. CoRR abs/1909.11406 (2019) - 2018
- [j92]Sofia Fernandes, Hadi Fanaee-T, João Gama:
Dynamic graph summarization: a tensor decomposition approach. Data Min. Knowl. Discov. 32(5): 1397-1420 (2018) - [j91]João Vinagre, Alípio Mário Jorge, João Gama:
Online bagging for recommender systems. Expert Syst. J. Knowl. Eng. 35(4) (2018) - [j90]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Improving acute kidney injury detection with conditional probabilities. Intell. Data Anal. 22(6): 1355-1374 (2018) - [j89]Pedro Pereira Rodrigues, João Araújo, João Gama, Luís M. B. Lopes:
A local algorithm to approximate the global clustering of streams generated in ubiquitous sensor networks. Int. J. Distributed Sens. Networks 14(10) (2018) - [j88]Pawel Matuszyk, João Vinagre, Myra Spiliopoulou, Alípio Mário Jorge, João Gama:
Forgetting techniques for stream-based matrix factorization in recommender systems. Knowl. Inf. Syst. 55(2): 275-304 (2018) - [j87]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
A comparison of hierarchical multi-output recognition approaches for anuran classification. Mach. Learn. 107(11): 1651-1671 (2018) - [j86]Fabíola Souza F. Pereira, João Gama, Sandra de Amo, Gina M. B. Oliveira:
On analyzing user preference dynamics with temporal social networks. Mach. Learn. 107(11): 1745-1773 (2018) - [j85]Ricardo Teixeira Sousa, João Gama:
Multi-label classification from high-speed data streams with adaptive model rules and random rules. Prog. Artif. Intell. 7(3): 177-187 (2018) - [j84]Luís Moreira-Matias, João Manuel Portela da Gama, Cristina Olaverri-Monreal, Rahul Nair, Roberto Trasarti:
Guest Editorial Special Issue on Knowledge Discovery From Mobility Data for Intelligent Transportation Systems. IEEE Trans. Intell. Transp. Syst. 19(11): 3626-3629 (2018) - [j83]Shazia Tabassum, Fabíola Souza F. Pereira, Sofia Fernandes, João Gama:
Cover Image, Volume 8, Issue 5. WIREs Data Mining Knowl. Discov. 8(5) (2018) - [j82]Shazia Tabassum, Fabíola Souza F. Pereira, Sofia Fernandes, João Gama:
Social network analysis: An overview. WIREs Data Mining Knowl. Discov. 8(5) (2018) - [c169]Carlos Fernandes, Luis Fonseca, Flora J. Ferreira, Miguel F. Gago, Luís Costa, Nuno J. Sousa, Carlos Abreu Ferreira, João Gama, Wolfram Erlhagen, Estela Bicho:
Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait. BIBM 2018: 2024-2030 - [c168]Shazia Tabassum, João Gama:
Biased Dynamic Sampling for Temporal Network Streams. COMPLEX NETWORKS (1) 2018: 512-523 - [c167]João Vinagre, Alípio Mário Jorge, João Gama:
Online Gradient Boosting for Incremental Recommender Systems. DS 2018: 209-223 - [c166]Bruno Veloso, João Gama, Benedita Malheiro:
Self Hyper-Parameter Tuning for Data Streams. DS 2018: 241-255 - [c165]Rui Portocarrero Sarmento, Mário Cordeiro, Pavel Brazdil, João Gama:
Incremental TextRank - Automatic Keyword Extraction for Text Streams. ICEIS (1) 2018: 363-370 - [c164]Jakub Zgraja, João Gama, Michal Wozniak:
Active Learning by Clustering for Drifted Data Stream Classification. DMLE/IOTSTREAMING@PKDD/ECML 2018: 80-90 - [c163]Bruno Veloso, João Gama, Benedita Malheiro, João Vinagre:
Self Hyper-parameter Tuning for Stream Recommendation Algorithms. DMLE/IOTSTREAMING@PKDD/ECML 2018: 91-102 - [c162]Richard Hugh Moulton, Herna L. Viktor, Nathalie Japkowicz, João Gama:
Clustering in the Presence of Concept Drift. ECML/PKDD (1) 2018: 339-355 - [c161]Ricardo Teixeira Sousa, João Gama:
Co-training study for online regression. SAC 2018: 529-531 - [c160]Bruno Veloso, Benedita Malheiro, Juan-Carlos Burguillo, Jeremy D. Foss, João Gama:
Personalised Dynamic Viewer Profiling for Streamed Data. WorldCIST (2) 2018: 501-510 - [e16]M. Arif Wani, Mehmed M. Kantardzic, Moamar Sayed Mouchaweh, João Gama, Edwin Lughofer:
17th IEEE International Conference on Machine Learning and Applications, ICMLA 2018, Orlando, FL, USA, December 17-20, 2018. IEEE 2018, ISBN 978-1-5386-6805-4 [contents] - [e15]Moamar Sayed Mouchaweh, Hamid Bouchachia, João Gama, Rita Paula Ribeiro:
Proceedings of the Workshop on Large-scale Learning from Data Streams in Evolving Environments (STREAMEVOLV 2016) co-located with the 2016 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD 2016), Riva del Garda, Italy, September 23, 2016. CEUR Workshop Proceedings 2069, CEUR-WS.org 2018 [contents] - [i8]Mário Cordeiro, Rui Portocarrero Sarmento, Pavel Brazdil, João Gama:
Dynamic Laplace: Efficient Centrality Measure for Weighted or Unweighted Evolving Networks. CoRR abs/1808.02960 (2018) - 2017
- [j81]Jonathan de Andrade Silva, Eduardo Raul Hruschka, João Gama:
An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Syst. Appl. 67: 228-238 (2017) - [j80]Raquel Sebastião, João Gama, Teresa Mendonça:
Fading histograms in detecting distribution and concept changes. Int. J. Data Sci. Anal. 3(3): 183-212 (2017) - [j79]Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, Michal Wozniak:
Ensemble learning for data stream analysis: A survey. Inf. Fusion 37: 132-156 (2017) - [j78]Douglas de O. Cardoso, João Gama, Felipe M. G. França:
Weightless neural networks for open set recognition. Mach. Learn. 106(9-10): 1547-1567 (2017) - [j77]João Gama, Eugénio C. Oliveira, Henrique Lopes Cardoso:
Computational Models for Social and Technical Interactions. New Gener. Comput. 35(4): 307-310 (2017) - [j76]Douglas de O. Cardoso, Felipe M. G. França, João Gama:
WCDS: A Two-Phase Weightless Neural System for Data Stream Clustering. New Gener. Comput. 35(4): 391-416 (2017) - [c159]Rui Portocarrero Sarmento, Mário Cordeiro, Pavel Brazdil, João Gama:
Efficient Incremental Laplace Centrality Algorithm for Dynamic Networks. COMPLEX NETWORKS 2017: 341-352 - [c158]Sofia Fernandes, Hadi Fanaee Tork, João Manuel Portela da Gama:
The Initialization and Parameter Setting Problem in Tensor Decomposition-Based Link Prediction. DSAA 2017: 99-108 - [c157]Hamid Eslami Nosratabadi, Hadi Fanaee-T, João Gama:
Mobility Mining Using Nonnegative Tensor Factorization. EPIA 2017: 321-330 - [c156]Saulo Ruiz, Pedro Gomes, Luís Rodrigues, João Gama:
Credit Scoring in Microfinance Using Non-traditional Data. EPIA 2017: 447-458 - [c155]João Vinagre, Alípio Mário Jorge, João Gama:
Improving Incremental Recommenders with Online Bagging. EPIA 2017: 597-607 - [c154]Ana Rita Nogueira, Carlos Abreu Ferreira, João Gama:
Acute Kidney Injury Detection: An Alarm System to Improve Early Treatment. ISMIS 2017: 57-63 - [c153]Ricardo Teixeira Sousa, João Gama:
Co-training Semi-supervised Learning for Single-Target Regression in Data Streams Using AMRules. ISMIS 2017: 499-508 - [c152]Eduarda Portela, Rita P. Ribeiro, João Gama:
Outliers and the Simpson's Paradox. MICAI (1) 2017: 267-278 - [c151]Ricardo Teixeira Sousa, João Gama:
Comparison Between Co-training and Self-training for Single-target Regression in Data Streams using AMRules. IOTSTREAMING@PKDD/ECML 2017 - [c150]João Duarte, João Gama:
Feature ranking in hoeffding algorithms for regression. SAC 2017: 836-841 - [e14]Eugénio C. Oliveira, João Gama, Zita A. Vale, Henrique Lopes Cardoso:
Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Porto, Portugal, September 5-8, 2017, Proceedings. Lecture Notes in Computer Science 10423, Springer 2017, ISBN 978-3-319-65339-6 [contents] - [e13]Ricard Gavaldà, Indre Zliobaite, João Gama:
Proceedings of the First Workshop on Data Science for Social Good co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Dicovery in Databases, SoGood@ECML-PKDD 2016, Riva del Garda, Italy, September 19, 2016. CEUR Workshop Proceedings 1831, CEUR-WS.org 2017 [contents] - [e12]Moamar Sayed Mouchaweh, Albert Bifet, Hamid Bouchachia, João Gama, Rita Paula Ribeiro:
Proceedings of the Workshop on IoT Large Scale Learning from Data Streams co-located with the 2017 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2017), Skopje, Macedonia, September 18-22, 2017. CEUR Workshop Proceedings 1958, CEUR-WS.org 2017 [contents] - [r4]João Gama:
Clustering from Data Streams. Encyclopedia of Machine Learning and Data Mining 2017: 226-231 - 2016
- [j75]Elaine R. Faria, Isabel J. C. R. Gonçalves, André C. P. L. F. de Carvalho, João Gama:
Novelty detection in data streams. Artif. Intell. Rev. 45(2): 235-269 (2016) - [j74]Luís Moreira-Matias, Oded Cats, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
An online learning approach to eliminate Bus Bunching in real-time. Appl. Soft Comput. 47: 460-482 (2016) - [j73]F. E. Correa, Márcia D. B. Oliveira, João Gama, Pedro Luiz Pizzigatti Corrêa, Jorge Rady de Almeida Jr.:
Analyzing the behavior dynamics of grain price indexes using Tucker tensor decomposition and spatio-temporal trajectories. Comput. Electron. Agric. 120: 72-78 (2016) - [j72]Elaine Ribeiro de Faria, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
MINAS: multiclass learning algorithm for novelty detection in data streams. Data Min. Knowl. Discov. 30(3): 640-680 (2016) - [j71]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Time-evolving O-D matrix estimation using high-speed GPS data streams. Expert Syst. Appl. 44: 275-288 (2016) - [j70]Maria Rocha Sousa, João Gama, Elísio Brandão:
A new dynamic modeling framework for credit risk assessment. Expert Syst. Appl. 45: 341-351 (2016) - [j69]Hanen Borchani, Pedro Larrañaga, João Gama, Concha Bielza:
Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. Intell. Data Anal. 20(2): 257-280 (2016) - [j68]Hadi Fanaee-T, João Gama:
Event detection from traffic tensors: A hybrid model. Neurocomputing 203: 22-33 (2016) - [j67]Hadi Fanaee-T, João Gama:
Tensor-based anomaly detection: An interdisciplinary survey. Knowl. Based Syst. 98: 130-147 (2016) - [j66]Rita P. Ribeiro, Pedro Mota Pereira, João Gama:
Sequential anomalies: a study in the Railway Industry. Mach. Learn. 105(1): 127-153 (2016) - [j65]Mário Cordeiro, Rui Sarmento, João Gama:
Dynamic community detection in evolving networks using locality modularity optimization. Soc. Netw. Anal. Min. 6(1): 15:1-15:20 (2016) - [j64]João Duarte, João Gama, Albert Bifet:
Adaptive Model Rules From High-Speed Data Streams. ACM Trans. Knowl. Discov. Data 10(3): 30:1-30:22 (2016) - [j63]Felipe Azevedo Pinage, Eulanda Miranda dos Santos, João Manuel Portela da Gama:
Classification systems in dynamic environments: an overview. WIREs Data Mining Knowl. Discov. 6(5): 156-166 (2016) - [c149]Mário Cordeiro, João Gama:
Online Social Networks Event Detection: A Survey. Solving Large Scale Learning Tasks 2016: 1-41 - [c148]Juan Gabriel Colonna, Tanel Peet, Carlos Abreu Ferreira, Alípio Mário Jorge, Elsa Ferreira Gomes, João Gama:
Automatic Classification of Anuran Sounds Using Convolutional Neural Networks. C3S2E 2016: 73-78 - [c147]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
How to Correctly Evaluate an Automatic Bioacoustics Classification Method. CAEPIA 2016: 37-47 - [c146]Ricardo Teixeira Sousa, João Gama:
Online Multi-label Classification with Adaptive Model Rules. CAEPIA 2016: 58-67 - [c145]Juan Gabriel Colonna, João Gama, Eduardo Freire Nakamura:
Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach. DS 2016: 198-212 - [c144]Shazia Tabassum, João Gama:
Evolution Analysis of Call Ego-Networks. DS 2016: 213-225 - [c143]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
On Using Temporal Networks to Analyze User Preferences Dynamics. DS 2016: 408-423 - [c142]Alípio M. Jorge, João Vinagre, Marcos Aurélio Domingues, João Gama, Carlos Soares, Pawel Matuszyk, Myra Spiliopoulou:
Scalable Online Top-N Recommender Systems. EC-Web 2016: 3-20 - [c141]Rita P. Ribeiro, Ricardo Oliveira, João Gama:
Detection of Fraud Symptoms in the Retail Industry. IBERAMIA 2016: 189-200 - [c140]Ricardo Teixeira Sousa, João Gama:
Online Semi-supervised Learning for Multi-target Regression in Data Streams Using AMRules. IDA 2016: 123-133 - [c139]Gianmarco De Francisci Morales, Albert Bifet, Latifur Khan, João Gama, Wei Fan:
IoT Big Data Stream Mining. KDD 2016: 2119-2120 - [c138]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
Evolving Centralities in Temporal Graphs: A Twitter Network Analysis. MDM (Workshops) 2016: 43-48 - [c137]Shazia Tabassum, João Gama:
Sampling Evolving Ego-Networks with forgetting Factor. MDM (Workshops) 2016: 55-59 - [c136]Luís Moreira-Matias, João Gama, João Mendes-Moreira:
Concept Neurons - Handling Drift Issues for Real-Time Industrial Data Mining. ECML/PKDD (3) 2016: 96-111 - [c135]Fabíola Souza F. Pereira, Sandra de Amo, João Gama:
Detecting Events in Evolving Social Networks through Node Centrality Analysis. STREAMEVOLV@ECML-PKDD 2016 - [c134]Ricardo Teixeira Sousa, João Gama:
First Principle Models Based Dataset Generation for Multi-Target Regression and Multi-Label Classification Evaluation. STREAMEVOLV@ECML-PKDD 2016 - [c133]João Vinagre, Alípio Mário Jorge, João Gama:
Online Bagging for Recommendation with Incremental Matrix Factorization. STREAMEVOLV@ECML-PKDD 2016 - [c132]Shazia Tabassum, João Gama:
Sampling massive streaming call graphs. SAC 2016: 923-928 - [c131]Douglas de O. Cardoso, Felipe Maia Galvão França, João Gama:
Clustering data streams using a forgetful neural model. SAC 2016: 949-951 - [i7]João Vinagre, Alípio Mário Jorge, João Gama:
Online bagging for recommendation with incremental matrix factorization. CoRR abs/1611.00558 (2016) - [i6]Hadi Fanaee-T, João Gama:
SimTensor: A synthetic tensor data generator. CoRR abs/1612.03772 (2016) - 2015
- [j62]Petr Kosina, João Gama:
Very fast decision rules for classification in data streams. Data Min. Knowl. Discov. 29(1): 168-202 (2015) - [j61]Carlos Sáez, Pedro Pereira Rodrigues, João Gama, Montserrat Robles, Juan Miguel García-Gómez:
Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Min. Knowl. Discov. 29(4): 950-975 (2015) - [j60]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. Data Min. Knowl. Discov. 29(5): 1113-1115 (2015) - [j59]Hadi Fanaee-T, João Gama:
Eigenspace method for spatiotemporal hotspot detection. Expert Syst. J. Knowl. Eng. 32(3): 454-464 (2015) - [j58]Hadi Fanaee-T, João Gama:
EigenEvent: An algorithm for event detection from complex data streams in syndromic surveillance. Intell. Data Anal. 19(3): 597-616 (2015) - [j57]Elena Ikonomovska, João Gama, Saso Dzeroski:
Online tree-based ensembles and option trees for regression on evolving data streams. Neurocomputing 150: 458-470 (2015) - [j56]João Mendes-Moreira, Luís Moreira-Matias, João Gama, Jorge Freire de Sousa:
Validating the coverage of bus schedules: A Machine Learning approach. Inf. Sci. 293: 299-313 (2015) - [j55]Hadi Fanaee-T, João Gama:
Multi-aspect-streaming tensor analysis. Knowl. Based Syst. 89: 332-345 (2015) - [j54]Concha Bielza, João Gama, Alípio Jorge, Indre Zliobaite:
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. Mach. Learn. 100(2-3): 157-159 (2015) - [j53]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Exploring multi-relational temporal databases with a propositional sequence miner. Prog. Artif. Intell. 4(1-2): 11-20 (2015) - [j52]Luís Moreira-Matias, João Mendes-Moreira, Jorge Freire de Sousa, João Gama:
Improving Mass Transit Operations by Using AVL-Based Systems: A Survey. IEEE Trans. Intell. Transp. Syst. 16(4): 1636-1653 (2015) - [j51]Elaine Ribeiro de Faria, Isabel Ribeiro Gonçalves, João Gama, André Carlos Ponce de Leon Ferreira de Carvalho:
Evaluation of Multiclass Novelty Detection Algorithms for Data Streams. IEEE Trans. Knowl. Data Eng. 27(11): 2961-2973 (2015) - [j50]João Vinagre, Alípio Mário Jorge, João Gama:
An overview on the exploitation of time in collaborative filtering. WIREs Data Mining Knowl. Discov. 5(5): 195-215 (2015) - [c130]Vânia Almeida, João Gama:
Measures for Combining Prediction Intervals Uncertainty and Reliability in Forecasting. CORES 2015: 147-157 - [c129]João Duarte, João Gama:
Multi-target regression from high-speed data streams with adaptive model rules. DSAA 2015: 1-10 - [c128]João Gama:
Keynote speaker 2: Real time data mining. EAIS 2015: 1 - [c127]Ana M. Silva, Rita P. Ribeiro, João Gama:
An Experimental Study on Predictive Models Using Hierarchical Time Series. EPIA 2015: 501-512 - [c126]Rui Sarmento, Mário Cordeiro, João Gama:
Streaming Networks Sampling using top-K Networks. ICEIS (1) 2015: 228-234 - [c125]Vítor Cerqueira, Márcia D. B. Oliveira, João Gama:
A Framework for Analysing Dynamic Communities in Large-scale Social Networks. ICEIS (1) 2015: 235-242 - [c124]Vinícius M. A. de Souza, Diego Furtado Silva, Gustavo E. A. P. A. Batista, João Gama:
Classification of Evolving Data Streams with Infinitely Delayed Labels. ICMLA 2015: 214-219 - [c123]Douglas de O. Cardoso, Felipe Maia Galvão França, João Gama:
A bounded neural network for open set recognition. IJCNN 2015: 1-7 - [c122]Yusuke Sakamoto, Ken-ichi Fukui, João Gama, Daniela Nicklas, Koichi Moriyama, Masayuki Numao:
Concept Drift Detection with Clustering via Statistical Change Detection Methods. KSE 2015: 37-42 - [c121]Pawel Matuszyk, João Vinagre, Myra Spiliopoulou, Alípio Mário Jorge, João Gama:
Forgetting methods for incremental matrix factorization in recommender systems. SAC 2015: 947-953 - [c120]Rui Sarmento, Mário Cordeiro, João Gama:
Visualization of evolving large scale ego-networks. SAC 2015: 960-962 - [c119]João Vinagre, Alípio Mário Jorge, João Gama:
Collaborative filtering with recency-based negative feedback. SAC 2015: 963-965 - [c118]Vinícius M. A. de Souza, Diego Furtado Silva, João Gama, Gustavo E. A. P. A. Batista:
Data Stream Classification Guided by Clustering on Nonstationary Environments and Extreme Verification Latency. SDM 2015: 873-881 - [e11]Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, Carlos Soares, João Gama, Alípio Jorge:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part I. Lecture Notes in Computer Science 9284, Springer 2015, ISBN 978-3-319-23527-1 [contents] - [e10]Annalisa Appice, Pedro Pereira Rodrigues, Vítor Santos Costa, João Gama, Alípio Jorge, Carlos Soares:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2015, Porto, Portugal, September 7-11, 2015, Proceedings, Part II. Lecture Notes in Computer Science 9285, Springer 2015, ISBN 978-3-319-23524-0 [contents] - [i5]João Vinagre, Alípio Mário Jorge, João Gama:
Evaluation of recommender systems in streaming environments. CoRR abs/1504.08175 (2015) - 2014
- [j49]João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia:
A survey on concept drift adaptation. ACM Comput. Surv. 46(4): 44:1-44:37 (2014) - [j48]Zoran Bosnic, Jaka Demsar, Grega Kespret, Pedro Pereira Rodrigues, João Gama, Igor Kononenko:
Enhancing data stream predictions with reliability estimators and explanation. Eng. Appl. Artif. Intell. 34: 178-192 (2014) - [j47]Rosane Maria Maffei Vallim, Jose Augusto Andrade Filho, Rodrigo Fernandes de Mello, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Unsupervised density-based behavior change detection in data streams. Intell. Data Anal. 18(2): 181-201 (2014) - [j46]João Gama, Petr Kosina:
Recurrent concepts in data streams classification. Knowl. Inf. Syst. 40(3): 489-507 (2014) - [j45]Hadi Fanaee-T, João Gama:
Event labeling combining ensemble detectors and background knowledge. Prog. Artif. Intell. 2(2-3): 113-127 (2014) - [j44]Raquel Sebastião, João Gama, Teresa Mendonça:
Constructing fading histograms from data streams. Prog. Artif. Intell. 3(1): 15-28 (2014) - [j43]Márcia D. B. Oliveira, Américo Guerreiro, João Gama:
Dynamic communities in evolving customer networks: an analysis using landmark and sliding windows. Soc. Netw. Anal. Min. 4(1): 208 (2014) - [j42]Pedro Pereira Rodrigues, João Gama:
Distributed clustering of ubiquitous data streams. WIREs Data Mining Knowl. Discov. 4(1): 38-54 (2014) - [j41]Mohamed Medhat Gaber, João Gama, Shonali Krishnaswamy, João Bártolo Gomes, Frederic T. Stahl:
Data stream mining in ubiquitous environments: state-of-the-art and current directions. WIREs Data Mining Knowl. Discov. 4(2): 116-138 (2014) - [c117]Anh Thu Vu, Gianmarco De Francisci Morales, João Gama, Albert Bifet:
Distributed Adaptive Model Rules for mining big data streams. IEEE BigData 2014: 345-353 - [c116]Cláudia Camila Dias, Cristina Granja, Altamiro da Costa Pereira, João Gama, Pedro Pereira Rodrigues:
Using Probabilistic Graphical Models to Enhance the Prognosis of Health-Related Quality of Life in Adult Survivors of Critical Illness. CBMS 2014: 56-61 - [c115]Pedro Mota Pereira, Rita P. Ribeiro, João Gama:
Failure Prediction - An Application in the Railway Industry. Discovery Science 2014: 264-275 - [c114]Raquel Sebastião, João Gama, Teresa Mendonça:
Comparing Data Distribution Using Fading Histograms. ECAI 2014: 1095-1096 - [c113]Vânia Almeida, João Gama:
Collaborative Wind Power Forecast. ICAIS 2014: 162-171 - [c112]João Gama:
Challenges in Learning from Streaming Data Extended Abstract. ICT Innovations 2014: 1-5 - [c111]Luís Moreira-Matias, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
An Incremental Probabilistic Model to Predict Bus Bunching in Real-Time. IDA 2014: 227-238 - [c110]João Gama:
Keynote speakers. ISCC 2014: 1 - [c109]Luís Moreira-Matias, Rafael Nunes, Michel Ferreira, João Mendes-Moreira, João Gama:
On Predicting a Call Center's Workload: A Discretization-Based Approach. ISMIS 2014: 548-553 - [c108]Luís Moreira-Matias, João Mendes-Moreira, Michel Ferreira, João Gama, Luís Damas:
An online learning framework for predicting the taxi stand's profitability. ITSC 2014: 2009-2014 - [c107]João Duarte, João Gama:
Ensembles of Adaptive Model Rules from High-Speed Data Streams. BigMine 2014: 198-213 - [c106]Rui Sarmento, Mário Cordeiro, João Gama:
Visualization for Streaming Telecommunications Networks. NFMCP 2014: 117-131 - [c105]João Vinagre, Alípio Mário Jorge, João Gama:
Fast Incremental Matrix Factorization for Recommendation with Positive-Only Feedback. UMAP 2014: 459-470 - [i4]Hadi Fanaee-T, João Gama:
An eigenvector-based hotspot detection. CoRR abs/1406.3191 (2014) - [i3]Hadi Fanaee-T, Márcia D. B. Oliveira, João Gama, Simon Malinowski, Ricardo Morla:
Event and Anomaly Detection Using Tucker3 Decomposition. CoRR abs/1406.3266 (2014) - [i2]Hadi Fanaee-T, João Gama:
EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance. CoRR abs/1406.3496 (2014) - [i1]Hadi Fanaee-T, João Gama:
Eigenspace Method for Spatiotemporal Hotspot Detection. CoRR abs/1406.3506 (2014) - 2013
- [j40]Jonathan de Andrade Silva, Elaine R. Faria, Rodrigo C. Barros, Eduardo R. Hruschka, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Data stream clustering: A survey. ACM Comput. Surv. 46(1): 13:1-13:31 (2013) - [j39]Márcia D. B. Oliveira, João Gama:
Visualization of evolving social networks using actor-level and community-level trajectories. Expert Syst. J. Knowl. Eng. 30(4): 306-319 (2013) - [j38]Raquel Sebastião, Margarida Martins da Silva, Rui Rabiço, João Gama, Teresa Mendonça:
Real-time algorithm for changes detection in depth of anesthesia signals. Evol. Syst. 4(1): 3-12 (2013) - [j37]João Gama:
Data Stream Mining: the Bounded Rationality. Informatica (Slovenia) 37(1): 21-25 (2013) - [j36]João Gama, Raquel Sebastião, Pedro Pereira Rodrigues:
On evaluating stream learning algorithms. Mach. Learn. 90(3): 317-346 (2013) - [j35]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Predicting Taxi-Passenger Demand Using Streaming Data. IEEE Trans. Intell. Transp. Syst. 14(3): 1393-1402 (2013) - [c104]Elaine R. Faria, Isabel J. C. R. Gonçalves, João Gama, André C. P. L. F. de Carvalho:
Evaluation Methodology for Multiclass Novelty Detection Algorithms. BRACIS 2013: 19-25 - [c103]Daniela Vasco, Pedro Pereira Rodrigues, João Gama:
Contextual anomalies in medical data. CBMS 2013: 544-545 - [c102]João Gama, Petr Kosina, Ezilda Almeida:
Avoiding Anomalies in Data Stream Learning. Discovery Science 2013: 49-63 - [c101]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
On Predicting the Taxi-Passenger Demand: A Real-Time Approach. EPIA 2013: 54-65 - [c100]Douglas de O. Cardoso, João Gama, Massimo De Gregorio, Felipe M. G. França, Maurizio Giordano, Priscila M. V. Lima:
WIPS: the WiSARD Indoor Positioning System. ESANN 2013 - [c99]Ezilda Almeida, Carlos Abreu Ferreira, João Gama:
Learning Model Rules From High-Speed Data Streams. UDM@IJCAI 2013: 10 - [c98]Luís Moreira-Matias, Ricardo Fernandes, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
On Recommending Urban Hotspots to Find Our Next Passenger. UDM@IJCAI 2013: 17 - [c97]Ezilda Almeida, Carlos Abreu Ferreira, João Gama:
Adaptive Model Rules from Data Streams. ECML/PKDD (1) 2013: 480-492 - [c96]Elaine R. Faria, João Gama, André C. P. L. F. de Carvalho:
Novelty detection algorithm for data streams multi-class problems. SAC 2013: 795-800 - [c95]Ezilda Almeida, Petr Kosina, João Gama:
Random rules from data streams. SAC 2013: 813-814 - [e9]Pedro Pereira Rodrigues, Mykola Pechenizkiy, João Gama, Ricardo Cruz-Correia, Jiming Liu, Agma J. M. Traina, Peter J. F. Lucas, Paolo Soda:
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, Porto, Portugal, June 20-22, 2013. IEEE Computer Society 2013, ISBN 978-1-4799-1053-3 [contents] - [e8]João Gama, Michael May, Nuno Cavalheiro Marques, Paulo Cortez, Carlos Abreu Ferreira:
Proceedings of the 3rd Workshop on Ubiquitous Data Mining co-located with the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013), Beijing, China, August 3, 2013. CEUR Workshop Proceedings 1088, CEUR-WS.org 2013 [contents] - 2012
- [j34]Márcia D. B. Oliveira, João Gama:
A framework to monitor clusters evolution applied to economy and finance problems. Intell. Data Anal. 16(1): 93-111 (2012) - [j33]João Gama:
A survey on learning from data streams: current and future trends. Prog. Artif. Intell. 1(1): 45-55 (2012) - [j32]Indre Zliobaite, Albert Bifet, Mohamed Medhat Gaber, Bogdan Gabrys, João Gama, Leandro L. Minku, Katarzyna Musial:
Next challenges for adaptive learning systems. SIGKDD Explor. 14(1): 48-55 (2012) - [j31]Márcia D. B. Oliveira, João Gama:
An overview of social network analysis. WIREs Data Mining Knowl. Discov. 2(2): 99-115 (2012) - [c94]Raquel Sebastião, Margarida Martins da Silva, João Gama, Teresa Mendonça:
Contributions to a decision support system based on depth of anesthesia signals. CBMS 2012: 1-6 - [c93]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa, Vladimiro Miranda, Audun Botterud:
Predicting Ramp Events with a Stream-Based HMM Framework. Discovery Science 2012: 224-238 - [c92]Melissa Rodrigues, João Gama, Carlos Abreu Ferreira:
Identifying Relationships in Transactional Data. IBERAMIA 2012: 81-90 - [c91]Luís Moreira-Matias, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
Online Predictive Model for Taxi Services. IDA 2012: 230-240 - [c90]Zaigham Faraz Siddiqui, Márcia D. B. Oliveira, João Gama, Myra Spiliopoulou:
Where Are We Going? Predicting the Evolution of Individuals. IDA 2012: 357-368 - [c89]Douglas de O. Cardoso, Massimo De Gregorio, Priscila Machado Vieira Lima, João Gama, Felipe Maia Galvão França:
A Weightless Neural Network-Based Approach for Stream Data Clustering. IDEAL 2012: 328-335 - [c88]Luís Moreira-Matias, Carlos Abreu Ferreira, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
Bus Bunching Detection by Mining Sequences of Headway Deviations. ICDM 2012: 77-91 - [c87]Luís Moreira-Matias, João Gama, Michel Ferreira, Luís Damas:
A predictive model for the passenger demand on a taxi network. ITSC 2012: 1014-1019 - [c86]Shonali Krishnaswamy, João Gama, Mohamed Medhat Gaber:
Mobile Data Stream Mining: From Algorithms to Applications. MDM 2012: 360-363 - [c85]Luís Moreira-Matias, João Mendes-Moreira, João Gama, Pavel Brazdil:
Text Categorization Using an Ensemble Classifier Based on a Mean Co-association Matrix. MLDM 2012: 525-539 - [c84]Petr Kosina, João Gama:
Handling Time Changing Data with Adaptive Very Fast Decision Rules. ECML/PKDD (1) 2012: 827-842 - [c83]Petr Kosina, João Gama:
Very Fast Decision Rules for multi-class problems. SAC 2012: 795-800 - [c82]Elaine R. Faria, Rodrigo C. Barros, João Gama, André C. P. L. F. de Carvalho:
Improving the offline clustering stage of data stream algorithms in scenarios with variable number of clusters. SAC 2012: 829-830 - [c81]Rosane Maria Maffei Vallim, Jose Augusto Andrade Filho, André C. P. L. F. de Carvalho, João Gama:
A Density-Based Clustering Approach for Behavior Change Detection in Data Streams. SBRN 2012: 37-42 - [c80]Michel Ferreira, Ricardo Fernandes, Hugo Conceição, Pedro Emanuel Rodrigues Gomes, Pedro M. d'Orey, Luís Moreira-Matias, João Gama, Fernanda Lima, Luís Damas:
Vehicular Sensing: Emergence of a Massive Urban Scanner. S-CUBE 2012: 1-14 - [c79]Luís Moreira-Matias, Ricardo Fernandes, João Gama, Michel Ferreira, João Mendes-Moreira, Luís Damas:
An online recommendation system for the taxi stand choice problem (Poster). VNC 2012: 173-180 - 2011
- [j30]Elena Ikonomovska, João Gama, Saso Dzeroski:
Learning model trees from evolving data streams. Data Min. Knowl. Discov. 23(1): 128-168 (2011) - [j29]João Gama, Michael May:
Ubiquitous Knowledge Discovery. Intell. Data Anal. 15(1): 1 (2011) - [j28]João Gama, Pedro Pereira Rodrigues, Luís M. B. Lopes:
Clustering distributed sensor data streams using local processing and reduced communication. Intell. Data Anal. 15(1): 3-28 (2011) - [j27]Jian Pei, João Gama, Qiang Yang, Ronghuai Huang, Xue Li:
Best papers from the Fifth International Conference on Advanced Data Mining and Applications (ADMA 2009). Knowl. Inf. Syst. 27(2): 163-164 (2011) - [c78]Joaquim Marques de Sá, Raquel Sebastião, João Gama, Tânia Fontes:
New Results on Minimum Error Entropy Decision Trees. CIARP 2011: 355-362 - [c77]Shonali Krishnaswamy, João Gama, Mohamed Medhat Gaber:
Advances in data stream mining for mobile and ubiquitous environments. CIKM 2011: 2607-2608 - [c76]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Constrained Sequential Pattern Knowledge in Multi-relational Learning. EPIA 2011: 282-296 - [c75]Márcia D. B. Oliveira, João Gama:
Visualizing the Evolution of Social Networks. EPIA 2011: 476-490 - [c74]Douglas de O. Cardoso, Priscila M. V. Lima, Massimo De Gregorio, João Gama, Felipe M. G. França:
Clustering data streams with weightless neural networks. ESANN 2011 - [c73]João Gama:
Reasoning about the Learning Process. EGC 2011: 7-8 - [c72]Elena Ikonomovska, Kurt Driessens, Saso Dzeroski, João Gama:
Adaptive Windowing for Online Learning from Multiple Inter-related Data Streams. ICDM Workshops 2011: 697-704 - [c71]Elena Ikonomovska, João Gama, Bernard Zenko, Saso Dzeroski:
Speeding-Up Hoeffding-Based Regression Trees With Options. ICML 2011: 537-544 - [c70]Zoran Bosnic, Pedro Pereira Rodrigues, Igor Kononenko, João Gama:
Correcting Streaming Predictions of an Electricity Load Forecast System Using a Prediction Reliability Estimate. ICMMI 2011: 343-350 - [c69]José M. Carmona-Cejudo, Manuel Baena-García, José del Campo-Ávila, Albert Bifet, João Gama, Rafael Morales Bueno:
Online Evaluation of Email Streaming Classifiers Using GNUsmail. IDA 2011: 90-100 - [c68]João Gama, Petr Kosina:
Learning about the Learning Process. IDA 2011: 162-172 - [c67]João Gama, Petr Kosina:
Learning Decision Rules from Data Streams. IJCAI 2011: 1255-1260 - [c66]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Predictive Sequence Miner in ILP Learning. ILP 2011: 130-144 - [c65]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Sequential Pattern Knowledge in Multi-Relational Learning. ISCIS 2011: 539-545 - [c64]Elena Ikonomovska, João Gama, Saso Dzeroski:
Incremental multi-target model trees for data streams. SAC 2011: 988-993 - [c63]Pedro Pereira Rodrigues, João Gama, João Araújo, Luís M. B. Lopes:
L2GClust: local-to-global clustering of stream sources. SAC 2011: 1006-1011 - [c62]José M. Carmona-Cejudo, Manuel Baena-García, Rafael Morales Bueno, João Gama, Albert Bifet:
Using GNUsmail to Compare Data Stream Mining Methods for On-line Email Classification. WAPA 2011: 12-18 - [e7]João Gama, Elizabeth Bradley, Jaakko Hollmén:
Advances in Intelligent Data Analysis X - 10th International Symposium, IDA 2011, Porto, Portugal, October 29-31, 2011. Proceedings. Lecture Notes in Computer Science 7014, Springer 2011, ISBN 978-3-642-24799-6 [contents] - 2010
- [b1]João Gama:
Knowledge Discovery from Data Streams. Chapman and Hall / CRC Data Mining and Knowledge Discovery Series, CRC Press 2010, ISBN 978-1-4398-2611-9, pp. I-XIX, 1-237 - [j26]Varun Chandola, Olufemi A. Omitaomu, Auroop R. Ganguly, Ranga Raju Vatsavai, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 12(2): 50-53 (2010) - [c61]Petr Kosina, João Gama, Raquel Sebastião:
Drift Severity Metric. ECAI 2010: 1119-1120 - [c60]Márcia D. B. Oliveira, João Gama:
Bipartite Graphs for Monitoring Clusters Transitions. IDA 2010: 114-124 - [c59]Luís Matias, João Gama, João Mendes-Moreira, Jorge Freire de Sousa:
Validation of both number and coverage of bus schedules using AVL data. ITSC 2010: 131-136 - [c58]Hillol Kargupta, João Gama, Wei Fan:
The next generation of transportation systems, greenhouse emissions, and data mining. KDD 2010: 1209-1212 - [c57]Rosane Maria Maffei Vallim, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Data Stream Mining Algorithms for Building Decision Models in a Computer Role-Playing Game Simulation. SBGames 2010: 108-116 - [c56]Márcia D. B. Oliveira, João Gama:
MEC - Monitoring Clusters' Transitions. STAIRS 2010: 212-224 - [p6]João Gama, Antoine Cornuéjols:
Resource Aware Distributed Knowledge Discovery. Ubiquitous Knowledge Discovery 2010: 40-60 - [p5]Milton Severo, João Gama:
Change Detection with Kalman Filter and CUSUM. Ubiquitous Knowledge Discovery 2010: 148-162 - [p4]João Gama, Pedro Pereira Rodrigues, Eduardo Jaques Spinosa, André Carlos Ponce de Leon Ferreira de Carvalho:
Knowledge Discovery from Data Streams. Web Intelligence and Security - Advances in Data and Text Mining Techniques for Detecting and Preventing Terrorist Activities on the Web 2010: 125-138 - [e6]Mohamed Medhat Gaber, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Auroop R. Ganguly:
Knowledge Discovery from Sensor Data, Second International Workshop, Sensor-KDD 2008, Las Vegas, NV, USA, August 24-27, 2008, Revised Selected Papers. Lecture Notes in Computer Science 5840, Springer 2010, ISBN 978-3-642-12518-8 [contents] - [r3]João Gama:
Clustering from Data Streams. Encyclopedia of Machine Learning 2010: 180-183
2000 – 2009
- 2009
- [j25]Gladys Castillo, João Gama:
Adaptive Bayesian network classifiers. Intell. Data Anal. 13(1): 39-59 (2009) - [j24]João Gama, Auroop R. Ganguly, Olufemi A. Omitaomu, Ranga Raju Vatsavai, Mohamed Medhat Gaber:
Knowledge discovery from data streams. Intell. Data Anal. 13(3): 403-404 (2009) - [j23]Eduardo Jaques Spinosa, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Novelty detection with application to data streams. Intell. Data Anal. 13(3): 405-422 (2009) - [j22]Pedro Pereira Rodrigues, João Gama:
A system for analysis and prediction of electricity-load streams. Intell. Data Anal. 13(3): 477-496 (2009) - [j21]Olufemi A. Omitaomu, Ranga Raju Vatsavai, Auroop R. Ganguly, Nitesh V. Chawla, João Gama, Mohamed Medhat Gaber:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 11(2): 84-87 (2009) - [c55]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
Sequential Pattern Mining in Multi-relational Datasets. CAEPIA 2009: 121-130 - [c54]Joaquim Marques de Sá, João Gama, Raquel Sebastião, Luís A. Alexandre:
Decision Trees Using the Minimum Entropy-of-Error Principle. CAIP 2009: 799-807 - [c53]Elena Ikonomovska, João Gama, Raquel Sebastião, Dejan Gjorgjevikj:
Regression Trees from Data Streams with Drift Detection. Discovery Science 2009: 121-135 - [c52]João Gama, Petr Kosina:
Tracking Recurring Concepts with Meta-learners. EPIA 2009: 423-434 - [c51]Raquel Sebastião, Pedro Pereira Rodrigues, João Gama:
Change Detection in Climate Data over the Iberian Peninsula. ICDM Workshops 2009: 248-253 - [c50]João Gama, Raquel Sebastião, Pedro Pereira Rodrigues:
Issues in evaluation of stream learning algorithms. KDD 2009: 329-338 - [c49]João Gama, Pedro Pereira Rodrigues, Raquel Sebastião:
Evaluating algorithms that learn from data streams. SAC 2009: 1496-1500 - [p3]João Gama, Pedro Pereira Rodrigues:
An Overview on Mining Data Streams. Foundations of Computational Intelligence (6) 2009: 29-45 - [e5]Ronghuai Huang, Qiang Yang, Jian Pei, João Gama, Xiaofeng Meng, Xue Li:
Advanced Data Mining and Applications, 5th International Conference, ADMA 2009, Beijing, China, August 17-19, 2009. Proceedings. Lecture Notes in Computer Science 5678, Springer 2009, ISBN 978-3-642-03347-6 [contents] - [e4]João Gama, Vítor Santos Costa, Alípio Mário Jorge, Pavel Brazdil:
Discovery Science, 12th International Conference, DS 2009, Porto, Portugal, October 3-5, 2009. Lecture Notes in Computer Science 5808, Springer 2009, ISBN 978-3-642-04746-6 [contents] - [e3]Olufemi A. Omitaomu, Auroop R. Ganguly, João Gama, Ranga Raju Vatsavai, Nitesh V. Chawla, Mohamed Medhat Gaber:
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data, Paris, France, June 28, 2009. ACM 2009, ISBN 978-1-60558-668-7 [contents] - [r2]João Gama, Pedro Pereira Rodrigues:
Data Streams. Encyclopedia of Data Warehousing and Mining 2009: 561-565 - [r1]João Gama, Pedro Pereira Rodrigues:
Learning from Data Streams. Encyclopedia of Data Warehousing and Mining 2009: 1137-1141 - 2008
- [j20]Ana Carolina Lorena, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
A review on the combination of binary classifiers in multiclass problems. Artif. Intell. Rev. 30(1-4): 19-37 (2008) - [j19]João Gama, Jesús S. Aguilar-Ruiz, Ralf Klinkenberg:
Knowledge discovery from data streams. Intell. Data Anal. 12(3): 251-252 (2008) - [j18]José del Campo-Ávila, Gonzalo Ramos-Jiménez, João Gama, Rafael Morales Bueno:
Improving the performance of an incremental algorithm driven by error margins. Intell. Data Anal. 12(3): 305-318 (2008) - [j17]Edgar Pimenta, João Gama, André Carlos Ponce de Leon Ferreira de Carvalho:
The Dimension of Ecocs for Multiclass Classification Problems. Int. J. Artif. Intell. Tools 17(3): 433-447 (2008) - [j16]Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, Auroop R. Ganguly:
Knowledge discovery from sensor data (SensorKDD). SIGKDD Explor. 10(2): 68-73 (2008) - [j15]Pedro Pereira Rodrigues, João Gama, João Pedro Pedroso:
Hierarchical Clustering of Time-Series Data Streams. IEEE Trans. Knowl. Data Eng. 20(5): 615-627 (2008) - [c48]Elena Ikonomovska, João Gama:
Learning Model Trees from Data Streams. Discovery Science 2008: 52-63 - [c47]Pedro Pereira Rodrigues, João Gama:
Robust Division in Clustering of Streaming Time Series. ECAI 2008: 172-176 - [c46]Pedro Pereira Rodrigues, João Gama, Zoran Bosnic:
Online Reliability Estimates for Individual Predictions in Data Streams. ICDM Workshops 2008: 36-45 - [c45]Carlos Abreu Ferreira, João Gama, Vítor Santos Costa:
RUSE-WARMR: Rule Selection for Classifier Induction in Multi-relational Data-Sets. ICTAI (1) 2008: 379-386 - [c44]Raquel Sebastião, João Gama, Pedro Pereira Rodrigues, João Bernardes:
Monitoring Incremental Histogram Distribution for Change Detection in Data Streams. KDD Workshop on Knowledge Discovery from Sensor Data 2008: 25-42 - [c43]Pedro Pereira Rodrigues, João Gama:
A Simple Dense Pixel Visualization for Mobile Sensor Data Mining. KDD Workshop on Knowledge Discovery from Sensor Data 2008: 175-189 - [c42]Pedro Pereira Rodrigues, João Gama, Luís M. B. Lopes:
Clustering Distributed Sensor Data Streams. ECML/PKDD (2) 2008: 282-297 - [c41]João Gama, André Carlos Ponce de Leon Ferreira de Carvalho, Jesús S. Aguilar-Ruiz:
Special track on Data Streams: editorial message. SAC 2008: 955 - [c40]Eduardo Jaques Spinosa, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
Cluster-based novel concept detection in data streams applied to intrusion detection in computer networks. SAC 2008: 976-980 - [c39]Raquel Sebastião, João Gama, Teresa Mendonça:
Learning from Data Streams: Synopsis and Change Detection. STAIRS 2008: 163-174 - [p2]Michael May, Bettina Berendt, Antoine Cornuéjols, João Gama, Fosca Giannotti, Andreas Hotho, Donato Malerba, Ernestina Menasalvas, Katharina Morik, Rasmus Ulslev Pedersen, Lorenza Saitta, Yücel Saygin, Assaf Schuster, Koen Vanhoof:
Research Challenges in Ubiquitous Knowledge Discovery. Next Generation of Data Mining 2008 - [p1]João Gama:
Issues and Challenges in Learning from Data Streams. Next Generation of Data Mining 2008 - 2007
- [j14]Pedro Pereira Rodrigues, João Gama:
Electricity load forecast using data streams techniques. Monde des Util. Anal. Données 36: 129-135 (2007) - [c38]Raquel Sebastião, João Gama:
Change Detection in Learning Histograms from Data Streams. EPIA Workshops 2007: 112-123 - [c37]Pedro Pereira Rodrigues, João Gama:
Semi-fuzzy Splitting in Online Divisive-Agglomerative Clustering. EPIA Workshops 2007: 133-144 - [c36]Edgar Pimenta, João Gama, André Carlos Ponce de Leon Ferreira de Carvalho:
Pursuing the Best ECOC Dimension for Multiclass Problems. FLAIRS 2007: 622-627 - [c35]João Gama, Pedro Pereira Rodrigues:
Stream-Based Electricity Load Forecast. PKDD 2007: 446-453 - [c34]Eduardo Jaques Spinosa, André Carlos Ponce de Leon Ferreira de Carvalho, João Gama:
OLINDDA: a cluster-based approach for detecting novelty and concept drift in data streams. SAC 2007: 448-452 - [c33]Carlos Pinto, João Gama:
Incremental discretization, application to data with concept drift. SAC 2007: 467-468 - 2006
- [j13]João Gama, Ricardo Fernandes, Ricardo Rocha:
Decision trees for mining data streams. Intell. Data Anal. 10(1): 23-45 (2006) - [j12]João Gama, Pedro Pereira Rodrigues, Jesús S. Aguilar-Ruiz:
An Overview on Learning from Data Streams. New Gener. Comput. 25(1): 1-4 (2006) - [c32]João Gama, Gladys Castillo:
Learning with Local Drift Detection. ADMA 2006: 42-55 - [c31]Milton Severo, João Gama:
Change Detection with Kalman Filter and CUSUM. Discovery Science 2006: 243-254 - [c30]Gladys Castillo, João Gama:
An Adaptive Prequential Learning Framework for Bayesian Network Classifiers. PKDD 2006: 67-78 - [c29]João Gama, Carlos Pinto:
Discretization from data streams: applications to histograms and data mining. SAC 2006: 662-667 - [c28]Pedro Pereira Rodrigues, João Gama, João Pedro Pedroso:
ODAC: Hierarchical Clustering of Time Series Data Streams. SDM 2006: 499-503 - 2005
- [j11]Jesús S. Aguilar-Ruiz, João Gama:
Data Streams J.UCS Special Issue. J. Univers. Comput. Sci. 11(8): 1349-1352 (2005) - [j10]João Gama, Pedro Medas:
Learning Decision Trees from Dynamic Data Streams. J. Univers. Comput. Sci. 11(8): 1353-1366 (2005) - [c27]Gladys Castillo, João Gama:
Bias Management of Bayesian Network Classifiers. Discovery Science 2005: 70-83 - [c26]João Gama, João Moura Pires, Margarida Cardoso, Nuno Cavalheiro Marques, Luís Cavique:
Introduction. EPIA 2005: 287-287 - [c25]João Gama, Pedro Medas, Pedro Pereira Rodrigues:
Learning decision trees from dynamic data streams. SAC 2005: 573-577 - [e2]João Gama, Rui Camacho, Pavel Brazdil, Alípio Jorge, Luís Torgo:
Machine Learning: ECML 2005, 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings. Lecture Notes in Computer Science 3720, Springer 2005, ISBN 3-540-29243-8 [contents] - [e1]Alípio Jorge, Luís Torgo, Pavel Brazdil, Rui Camacho, João Gama:
Knowledge Discovery in Databases: PKDD 2005, 9th European Conference on Principles and Practice of Knowledge Discovery in Databases, Porto, Portugal, October 3-7, 2005, Proceedings. Lecture Notes in Computer Science 3721, Springer 2005, ISBN 3-540-29244-6 [contents] - 2004
- [j9]Miroslav Kubat, João Gama, Paul E. Utgoff:
Incremental learning and concept drift: Editor's introduction. Intell. Data Anal. 8(3): 211-212 (2004) - [j8]Alexandros Kalousis, João Gama, Melanie Hilario:
On Data and Algorithms: Understanding Inductive Performance. Mach. Learn. 54(3): 275-312 (2004) - [j7]João Gama:
Functional Trees. Mach. Learn. 55(3): 219-250 (2004) - [c24]João Gama, Pedro Medas:
Learning in Dynamic Environments: Decision Trees for Data Streams. PRIS 2004: 149-158 - [c23]João Gama, Pedro Medas, Ricardo Rocha:
Forest trees for on-line data. SAC 2004: 632-636 - [c22]João Gama, Pedro Medas, Gladys Castillo, Pedro Pereira Rodrigues:
Learning with Drift Detection. SBIA 2004: 286-295 - 2003
- [j6]João Gama:
Iterative Bayes. Theor. Comput. Sci. 292(2): 417-430 (2003) - [c21]Gladys Castillo, João Gama, Pedro Medas:
Adaptation to Drifting Concepts. EPIA 2003: 279-293 - [c20]João Gama, Ricardo Rocha, Pedro Medas:
Accurate decision trees for mining high-speed data streams. KDD 2003: 523-528 - [c19]Gladys Castillo, João Gama, Ana M. Breda:
Adaptive Bayes for a Student Modeling Prediction Task Based on Learning Styles. User Modeling 2003: 328-332 - 2002
- [c18]João Gama, Gladys Castillo:
Adaptive Bayes. IBERAMIA 2002: 765-774 - [c17]João Gama:
An Analysis of Functional Trees. ICML 2002: 155-162 - 2001
- [c16]João Gama:
Functional Trees. Discovery Science 2001: 59-73 - [c15]Nuno Amado, João Gama, Fernando M. A. Silva:
Parallel Implementation of Decision Tree Learning Algorithms. EPIA 2001: 6-13 - [c14]João Gama:
Functional Trees for Classification. ICDM 2001: 147-154 - [c13]João Gama:
Functional Trees for Regression. IDA 2001: 156-166 - 2000
- [j5]João Gama:
Thesis: Inductive learning classification algorithms. AI Commun. 13(2): 135-136 (2000) - [j4]João Gama:
Iterative Bayes. Intell. Data Anal. 4(6): 475-488 (2000) - [j3]João Gama, Pavel Brazdil:
Cascade Generalization. Mach. Learn. 41(3): 315-343 (2000) - [c12]João Gama:
A Linear-Bayes Classifier. IBERAMIA-SBIA 2000: 269-279
1990 – 1999
- 1999
- [j2]João Gama, Pavel Brazdil:
Linear tree. Intell. Data Anal. 3(1): 1-22 (1999) - [c11]João Gama:
Iterative Naive Bayes. Discovery Science 1999: 80-91 - [c10]João Gama:
Discriminant Trees. ICML 1999: 134-142 - 1998
- [c9]João Gama:
Combining Classifiers by Constructive Induction. ECML 1998: 178-189 - [c8]João Gama, Luís Torgo, Carlos Soares:
Dynamic Discretization of Continuous Attributes. IBERAMIA 1998: 160-169 - [c7]João Gama:
Local Cascade Generalization. ICML 1998: 206-214 - 1997
- [j1]Luís Torgo, João Gama:
Regression Using Classification Algorithms. Intell. Data Anal. 1(1-4): 275-292 (1997) - [c6]Luís Torgo, João Gama:
Search-Based Class Discretization. ECML 1997: 266-273 - [c5]João Gama:
Probabilistic Linear Tree. ICML 1997: 134-142 - [c4]João Gama:
Oblique Linear Tree. IDA 1997: 187-198 - 1996
- [c3]Luís Torgo, João Gama:
Regression by Classification. SBIA 1996: 51-60 - 1995
- [c2]João Gama, Pavel Brazdil:
Characterization of Classification Algorithms. EPIA 1995: 189-200 - 1994
- [c1]Pavel Brazdil, João Gama, Bob Henery:
Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning. ECML 1994: 83-102
Coauthor Index
André C. P. L. F. de Carvalho
aka: André Carlos Ponce de Leon Ferreira de Carvalho
aka: André Carlos Ponce de Leon Ferreira de Carvalho
Hadi Fanaee-T
aka: Hadi Fanaee Tork
aka: Hadi Fanaee Tork
Rita P. Ribeiro
aka: Rita Paula Ribeiro
aka: Rita Paula Ribeiro
[c246] [i41] [j133] [c243] [c242] [c241] [c236] [c232] [c231] [c229] [i31] [i30] [i29] [i28] [c225] [c224] [c222] [c221] [c220] [c215] [e32] [e31] [e30] [d3] [i24] [j126] [c210] [c209] [c208] [c207] [c205] [c204] [c203] [c202] [d2] [d1] [i20] [i19] [j119] [j115] [c201] [c199] [c193] [e25] [e24] [c183] [e22] [j100] [c172] [e17] [e15] [c152] [e12] [j66] [c141] [c127] [c115]
Rui Sarmento
aka: Rui Portocarrero Sarmento
aka: Rui Portocarrero Sarmento
[j138] [c246] [i40] [j136] [j135] [j133] [c241] [c237] [c232] [c231] [d4] [i31] [i30] [j130] [c222] [c218] [c215] [d3] [i26] [i24] [j126] [c212] [c210] [c206] [c204] [c203] [c202] [d2] [d1] [i20] [i18] [j117] [j115] [c201] [c199] [c194] [c193] [j108] [j103] [c191] [c185] [c184] [c183] [c182] [c173] [c166] [c163] [c160]
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