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ORCIDPeter A. Flach
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- affiliation: University of Bristol, Department of Computer Science, UK
- affiliation: Tilburg University, The Netherlands
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2020 – today
- 2024
[j61]Kacper Sokol
, Peter A. Flach:
Interpretable representations in explainable AI: from theory to practice. Data Min. Knowl. Discov. 38(5): 3102-3140 (2024)- [c116]Paul-Gauthier Noé, Miquel Perelló-Nieto, Jean-François Bonastre, Peter A. Flach:
Explaining a Probabilistic Prediction on the Simplex with Shapley Compositions. ECAI 2024: 1124-1131 - [i35]Paul-Gauthier Noé, Miquel Perelló-Nieto, Jean-François Bonastre, Peter A. Flach:
Explaining a probabilistic prediction on the simplex with Shapley compositions. CoRR abs/2408.01382 (2024) - 2023
- [j60]Katarzyna Stawarz, Dmitri S. Katz, Amid Ayobi, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-designing opportunities for Human-Centred Machine Learning in supporting Type 1 diabetes decision-making. Int. J. Hum. Comput. Stud. 173: 103003 (2023) - [j59]Haixia Bi, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Peter A. Flach, Ian Craddock:
An active semi-supervised deep learning model for human activity recognition. J. Ambient Intell. Humaniz. Comput. 14(10): 13049-13065 (2023) - [j58]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier calibration: a survey on how to assess and improve predicted class probabilities. Mach. Learn. 112(9): 3211-3260 (2023) - [c115]Matthew Clifford, Jonathan Erskine, Alexander Hepburn, Peter A. Flach, Raúl Santos-Rodríguez:
Reconciling Training and Evaluation Objectives in Location Agnostic Surrogate Explainers. CIKM 2023: 3833-3837 - [c114]Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Weisong Yang, Peter A. Flach:
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. PerCom Workshops 2023: 527-533 - [p1]Peter A. Flach, Kacper Sokol, Jan Wielemaker:
Simply Logical - The First Three Decades. Prolog: The Next 50 Years 2023: 184-193 - [i34]Taku Yamagata, Emma L. Tonkin, Benjamin Arana Sanchez, Ian Craddock, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Weisong Yang, Peter A. Flach:
When the Ground Truth is not True: Modelling Human Biases in Temporal Annotations. CoRR abs/2302.02706 (2023) - [i33]Tashi Namgyal, Peter A. Flach, Raúl Santos-Rodríguez:
MIDI-Draw: Sketching to Control Melody Generation. CoRR abs/2305.11605 (2023) - [i32]Torty Sivill, Peter A. Flach:
Shapley Sets: Feature Attribution via Recursive Function Decomposition. CoRR abs/2307.01777 (2023) - 2022
- [j57]Peter A. Flach:
Empirical Evaluation of Predictive Models: A keynote at ECIR 2022. SIGIR Forum 56(1): 2:1-2:5 (2022) - [j56]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python toolbox for algorithmic fairness, accountability and transparency. Softw. Impacts 14: 100406 (2022) - [c113]Torty Sivill, Peter A. Flach:
LIMESegment: Meaningful, Realistic Time Series Explanations. AISTATS 2022: 3418-3433 - [c112]Taku Yamagata, Raúl Santos-Rodríguez, Robert J. Piechocki, Peter A. Flach:
Understanding Reinforcement Learning Based Localisation as a Probabilistic Inference Algorithm. ICANN (2) 2022: 111-122 - [c111]Yu Chen, Peter A. Flach:
Self-Enhancer: A Self-supervised Framework for Low-Supervision, Drifted Data with Significant Missing Values. ICANN (4) 2022: 455-458 - [c110]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. PerCom Workshops 2022: 218-223 - [d3]Peter A. Flach, Kacper Sokol:
Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). Zenodo, 2022
- [d2]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodriguez, Peter A. Flach:
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. Zenodo, 2022 - [i31]Rafael Poyiadzi, Daniel Bacaicoa-Barber, Jesús Cid-Sueiro, Miquel Perelló-Nieto, Peter A. Flach, Raúl Santos-Rodríguez:
The Weak Supervision Landscape. CoRR abs/2203.16282 (2022) - [i30]Peter A. Flach, Kacper Sokol:
Simply Logical - Intelligent Reasoning by Example (Fully Interactive Online Edition). CoRR abs/2208.06823 (2022) - [i29]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. CoRR abs/2209.03805 (2022) - [i28]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
What and How of Machine Learning Transparency: Building Bespoke Explainability Tools with Interoperable Algorithmic Components. CoRR abs/2209.03813 (2022) - 2021
- [j55]Hao Song, Peter A. Flach:
Efficient and Robust Model Benchmarks with Item Response Theory and Adaptive Testing. Int. J. Interact. Multim. Artif. Intell. 6(5): 110-118 (2021) - [j54]Reem Alotaibi, Peter A. Flach:
Multi-label thresholding for cost-sensitive classification. Neurocomputing 436: 232-247 (2021) - [j53]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Co-Designing Personal Health? Multidisciplinary Benefits and Challenges in Informing Diabetes Self-Care Technologies. Proc. ACM Hum. Comput. Interact. 5(CSCW2): 457:1-457:26 (2021) - [j52]Haixia Bi, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Peter A. Flach:
Human Activity Recognition Based on Dynamic Active Learning. IEEE J. Biomed. Health Informatics 25(4): 922-934 (2021) - [j51]Fernando Martínez-Plumed, Lidia Contreras Ochando, Cèsar Ferri, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana, Peter A. Flach:
CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories. IEEE Trans. Knowl. Data Eng. 33(8): 3048-3061 (2021) - [c109]Amid Ayobi, Katarzyna Stawarz, Dmitri S. Katz, Paul Marshall, Taku Yamagata, Raúl Santos-Rodríguez, Peter A. Flach, Aisling Ann O'Kane:
Machine Learning Explanations as Boundary Objects: How AI Researchers Explain and Non-Experts Perceive Machine Learning. IUI Workshops 2021 - [i27]Yu Chen, Song Liu, Tom Diethe, Peter A. Flach:
Continual Density Ratio Estimation in an Online Setting. CoRR abs/2103.05276 (2021) - [i26]Kacper Sokol, Peter A. Flach:
You Only Write Thrice: Creating Documents, Computational Notebooks and Presentations From a Single Source. CoRR abs/2107.06639 (2021) - [i25]Stefan Radic Webster, Peter A. Flach:
Risk Sensitive Model-Based Reinforcement Learning using Uncertainty Guided Planning. CoRR abs/2111.04972 (2021) - [i24]Telmo de Menezes e Silva Filho, Hao Song, Miquel Perelló-Nieto, Raúl Santos-Rodríguez, Meelis Kull, Peter A. Flach:
Classifier Calibration: How to assess and improve predicted class probabilities: a survey. CoRR abs/2112.10327 (2021) - [i23]Kacper Sokol, Peter A. Flach:
Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence. CoRR abs/2112.14466 (2021) - 2020
- [j50]Marvin Meeng, Harm de Vries, Peter A. Flach, Siegfried Nijssen, Arno J. Knobbe:
Uni- and multivariate probability density models for numeric subgroup discovery. Intell. Data Anal. 24(6): 1403-1439 (2020) - [j49]Michael Holmes, Miquel Perelló-Nieto, Hao Song, Emma Tonkin, Sabrina Grant, Peter A. Flach:
Modelling Patient Behaviour Using IoT Sensor Data: a Case Study to Evaluate Techniques for Modelling Domestic Behaviour in Recovery from Total Hip Replacement Surgery. J. Heal. Informatics Res. 4(3): 238-260 (2020) - [j48]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. J. Open Source Softw. 5(49): 1904 (2020) - [j47]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All. Künstliche Intell. 34(2): 235-250 (2020) - [j46]Peter A. Flach:
Reflections on reciprocity in research. Mach. Learn. 109(7): 1281-1285 (2020) - [c108]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodríguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. AIES 2020: 344-350 - [c107]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1 Diabetes Blood Glucose Control. AAI4H@ECAI 2020: 72-77 - [c106]Kacper Sokol, Peter A. Flach:
Explainability fact sheets: a framework for systematic assessment of explainable approaches. FAT* 2020: 56-67 - [c105]Haixia Bi, Raúl Santos-Rodríguez, Peter A. Flach:
Polsar Image Classification via Robust Low-Rank Feature Extraction and Markov Random Field. IGARSS 2020: 708-711 - [d1]Kacper Sokol, Alexander Hepburn, Rafael Poyiadzi, Matthew Clifford, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Implementing and Deploying Fairness, Accountability and Transparency Algorithms in Predictive Systems. Zenodo, 2020 - [i22]Kacper Sokol, Peter A. Flach:
One Explanation Does Not Fit All: The Promise of Interactive Explanations for Machine Learning Transparency. CoRR abs/2001.09734 (2020) - [i21]Kacper Sokol, Peter A. Flach:
LIMEtree: Interactively Customisable Explanations Based on Local Surrogate Multi-output Regression Trees. CoRR abs/2005.01427 (2020) - [i20]Yu Chen, Tom Diethe, Peter A. Flach:
Bypassing Gradients Re-Projection with Episodic Memories in Online Continual Learning. CoRR abs/2006.11234 (2020) - [i19]Kacper Sokol, Peter A. Flach:
Towards Faithful and Meaningful Interpretable Representations. CoRR abs/2008.07007 (2020) - [i18]Taku Yamagata, Aisling Ann O'Kane, Amid Ayobi, Dmitri S. Katz, Katarzyna Stawarz, Paul Marshall, Peter A. Flach, Raúl Santos-Rodríguez:
Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Control. CoRR abs/2010.06266 (2020)
2010 – 2019
- 2019
- [j45]Tom Wilcox, Nanlin Jin, Peter A. Flach, Joshua Thumim:
A Big Data platform for smart meter data analytics. Comput. Ind. 105: 250-259 (2019) - [j44]Cèsar Ferri, José Hernández-Orallo, Peter A. Flach:
Setting decision thresholds when operating conditions are uncertain. Data Min. Knowl. Discov. 33(4): 805-847 (2019) - [j43]Niall Twomey, Haoyan Chen, Tom Diethe, Peter A. Flach:
An application of hierarchical Gaussian processes to the detection of anomalies in star light curves. Neurocomputing 342: 152-163 (2019) - [c104]Peter A. Flach:
Performance Evaluation in Machine Learning: The Good, the Bad, the Ugly, and the Way Forward. AAAI 2019: 9808-9814 - [c103]Kacper Sokol, Peter A. Flach:
Counterfactual Explanations of Machine Learning Predictions: Opportunities and Challenges for AI Safety. SafeAI@AAAI 2019 - [c102]Kacper Sokol, Peter A. Flach:
Desiderata for Interpretability: Explaining Decision Tree Predictions with Counterfactuals. AAAI 2019: 10035-10036 - [c101]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
$β^3$-IRT: A New Item Response Model and its Applications. AISTATS 2019: 1013-1021 - [c100]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution calibration for regression. ICML 2019: 5897-5906 - [c99]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration. NeurIPS 2019: 12295-12305 - [i17]Yu Chen, Telmo de Menezes e Silva Filho, Ricardo B. C. Prudêncio, Tom Diethe, Peter A. Flach:
β3-IRT: A New Item Response Model and its Applications. CoRR abs/1903.04016 (2019) - [i16]Hao Song, Tom Diethe, Meelis Kull, Peter A. Flach:
Distribution Calibration for Regression. CoRR abs/1905.06023 (2019) - [i15]Tom Diethe, Meelis Kull, Niall Twomey, Kacper Sokol, Hao Song, Miquel Perelló-Nieto, Emma Tonkin, Peter A. Flach:
HyperStream: a Workflow Engine for Streaming Data. CoRR abs/1908.02858 (2019) - [i14]Kacper Sokol, Raúl Santos-Rodríguez, Peter A. Flach:
FAT Forensics: A Python Toolbox for Algorithmic Fairness, Accountability and Transparency. CoRR abs/1909.05167 (2019) - [i13]Rafael Poyiadzi, Kacper Sokol, Raúl Santos-Rodriguez, Tijl De Bie, Peter A. Flach:
FACE: Feasible and Actionable Counterfactual Explanations. CoRR abs/1909.09369 (2019) - [i12]Meelis Kull, Miquel Perelló-Nieto, Markus Kängsepp, Telmo de Menezes e Silva Filho, Hao Song, Peter A. Flach:
Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration. CoRR abs/1910.12656 (2019) - [i11]Kacper Sokol, Alexander Hepburn, Raúl Santos-Rodríguez, Peter A. Flach:
bLIMEy: Surrogate Prediction Explanations Beyond LIME. CoRR abs/1910.13016 (2019) - [i10]Kacper Sokol, Peter A. Flach:
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches. CoRR abs/1912.05100 (2019) - 2018
- [j42]Peter A. Flach, Myra Spiliopoulou, Serge Allegrezza, Matthias Böhmer, Burkhard Hess, Berthold Lausen:
Introduction to the special issue on Data Science in Europe. Int. J. Data Sci. Anal. 6(3): 163-165 (2018) - [j41]Niall Twomey, Tom Diethe, Xenofon Fafoutis, Atis Elsts, Ryan McConville, Peter A. Flach, Ian Craddock:
A Comprehensive Study of Activity Recognition Using Accelerometers. Informatics 5(2): 27 (2018) - [j40]Przemyslaw Woznowski, Emma Tonkin, Peter A. Flach:
Activities of Daily Living Ontology for Ubiquitous Systems: Development and Evaluation. Sensors 18(7): 2361 (2018) - [c98]Haoyan Chen, Tom Diethe, Niall Twomey, Peter A. Flach:
Anomaly detection in star light curves using hierarchical Gaussian processes. ESANN 2018 - [c97]Mike Holmes, Hao Song, Emma Tonkin, Miquel Perelló-Nieto, Sabrina Grant, Peter A. Flach:
Analysis of Patient Domestic Activity in Recovery From Hip or Knee RePlacement Surgery: Modelling Wrist-worn Wearable RSSI and Accelerometer Data in The Wild. KDH@IJCAI 2018: 13-20 - [c96]Fernando Martínez-Plumed, Bao Sheng Loe, Peter A. Flach, Seán Ó hÉigeartaigh, Karina Vold, José Hernández-Orallo:
The Facets of Artificial Intelligence: A Framework to Track the Evolution of AI. IJCAI 2018: 5180-5187 - [c95]Kacper Sokol, Peter A. Flach:
Conversational Explanations of Machine Learning Predictions Through Class-contrastive Counterfactual Statements. IJCAI 2018: 5785-5786 - [c94]Kacper Sokol, Peter A. Flach:
Glass-Box: Explaining AI Decisions With Counterfactual Statements Through Conversation With a Voice-enabled Virtual Assistant. IJCAI 2018: 5868-5870 - [c93]Tom Diethe, Mike Holmes, Meelis Kull, Miquel Perelló-Nieto, Kacper Sokol, Hao Song, Emma Tonkin, Niall Twomey, Peter A. Flach:
Releasing eHealth Analytics into the Wild: Lessons Learnt from the SPHERE Project. KDD 2018: 243-252 - [i9]Hao Song, Meelis Kull, Peter A. Flach:
Non-Parametric Calibration of Probabilistic Regression. CoRR abs/1806.07690 (2018) - 2017
- [j39]Simon Price, Peter A. Flach:
Computational support for academic peer review: a perspective from artificial intelligence. Commun. ACM 60(3): 70-79 (2017) - [j38]Niall Twomey, Tom Diethe, Ian Craddock, Peter A. Flach:
Unsupervised learning of sensor topologies for improving activity recognition in smart environments. Neurocomputing 234: 93-106 (2017) - [c92]Meelis Kull, Telmo de Menezes e Silva Filho, Peter A. Flach:
Beta calibration: a well-founded and easily implemented improvement on logistic calibration for binary classifiers. AISTATS 2017: 623-631 - [c91]Kacper Sokol, Peter A. Flach:
The Role of Textualisation and Argumentation in Understanding the Machine Learning Process. IJCAI 2017: 5211-5212 - [r5]Peter A. Flach:
Classifier Calibration. Encyclopedia of Machine Learning and Data Mining 2017: 210-217 - [r4]Peter A. Flach:
First-Order Logic. Encyclopedia of Machine Learning and Data Mining 2017: 515-521 - [r3]Peter A. Flach:
ROC Analysis. Encyclopedia of Machine Learning and Data Mining 2017: 1109-1116 - [i8]Tom Diethe, Niall Twomey, Meelis Kull, Peter A. Flach, Ian Craddock:
Probabilistic Sensor Fusion for Ambient Assisted Living. CoRR abs/1702.01209 (2017) - [i7]Fernando Martínez-Plumed, Lidia Contreras Ochando, César Ferri, Peter A. Flach, José Hernández-Orallo, Meelis Kull, Nicolas Lachiche, María José Ramírez-Quintana:
CASP-DM: Context Aware Standard Process for Data Mining. CoRR abs/1709.09003 (2017) - 2016
- [j37]José Hernández-Orallo, Adolfo Martínez Usó, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach, Chowdhury Farhan Ahmed, Nicolas Lachiche:
Reframing in context: A systematic approach for model reuse in machine learning. AI Commun. 29(5): 551-566 (2016) - [j36]Niall Twomey, Tom Diethe, Peter A. Flach:
On the need for structure modelling in sequence prediction. Mach. Learn. 104(2-3): 291-314 (2016) - [j35]Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown:
Cost-sensitive boosting algorithms: Do we really need them? Mach. Learn. 104(2-3): 359-384 (2016) - [j34]Reem Al-Otaibi, Nanlin Jin, Tom Wilcox, Peter A. Flach:
Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data. IEEE Trans. Ind. Informatics 12(2): 645-654 (2016) - [c90]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
Declaratively Capturing Local Label Correlations with Multi-Label Trees. ECAI 2016: 1467-1475 - [c89]Tom Diethe, Niall Twomey, Peter A. Flach:
Active transfer learning for activity recognition. ESANN 2016 - [c88]Miquel Perelló-Nieto, Telmo de Menezes e Silva Filho, Meelis Kull, Peter A. Flach:
Background Check: A General Technique to Build More Reliable and Versatile Classifiers. ICDM 2016: 1143-1148 - [c87]Yu Chen, Tom Diethe, Peter A. Flach:
ADL™: A Topic Model for Discovery of Activities of Daily Living in a Smart Home. IJCAI 2016: 1404-1410 - [c86]Kacper Sokol, Peter A. Flach:
Activity Recognition in Multiple Contexts for Smart-House Data. ILP (Short Papers) 2016: 66-72 - [c85]Denis Moreira dos Reis, Peter A. Flach, Stan Matwin, Gustavo E. A. P. A. Batista:
Fast Unsupervised Online Drift Detection Using Incremental Kolmogorov-Smirnov Test. KDD 2016: 1545-1554 - [c84]Tom Diethe, Niall Twomey, Peter A. Flach:
BDL.NET: Bayesian dictionary learning in Infer.NET. MLSP 2016: 1-6 - [c83]Hao Song, Meelis Kull, Peter A. Flach, Georgios Kalogridis:
Subgroup Discovery with Proper Scoring Rules. ECML/PKDD (2) 2016: 492-510 - [i6]Niall Twomey, Tom Diethe, Meelis Kull, Hao Song, Massimo Camplani, Sion L. Hannuna, Xenofon Fafoutis, Ni Zhu, Pete Woznowski, Peter A. Flach, Ian Craddock:
The SPHERE Challenge: Activity Recognition with Multimodal Sensor Data. CoRR abs/1603.00797 (2016) - 2015
- [j33]Ni Zhu, Tom Diethe, Massimo Camplani, Lili Tao, Alison Burrows, Niall Twomey, Dritan Kaleshi, Majid Mirmehdi, Peter A. Flach, Ian Craddock:
Bridging e-Health and the Internet of Things: The SPHERE Project. IEEE Intell. Syst. 30(4): 39-46 (2015) - [j32]Cèsar Ferri Ramirez, Peter A. Flach, Nicolas Lachiche:
Report of the First International Workshop on Learning over Multiple Contexts (LMCE 2014). SIGKDD Explor. 17(1): 48-50 (2015) - [c82]Chowdhury Farhan Ahmed, Md. Samiullah, Nicolas Lachiche, Meelis Kull, Peter A. Flach:
Reframing in Frequent Pattern Mining. ICTAI 2015: 799-806 - [c81]Megha Agarwal, Peter A. Flach:
Activity recognition using conditional random field. iWOAR 2015: 4:1-4:8 - [c80]Peter A. Flach, Meelis Kull:
Precision-Recall-Gain Curves: PR Analysis Done Right. NIPS 2015: 838-846 - [c79]Reem Al-Otaibi, Ricardo B. C. Prudêncio, Meelis Kull, Peter A. Flach:
Versatile Decision Trees for Learning Over Multiple Contexts. ECML/PKDD (1) 2015: 184-199 - [c78]Yu Chen, Peter A. Flach:
SVR-based Modelling for the MoReBikeS Challenge: Analysis, Visualisation and Prediction. DC@PKDD/ECML 2015 - [c77]Meelis Kull, Peter A. Flach:
Novel Decompositions of Proper Scoring Rules for Classification: Score Adjustment as Precursor to Calibration. ECML/PKDD (1) 2015: 68-85 - [c76]Tom Diethe, Niall Twomey, Peter A. Flach:
Bayesian Modelling of the Temporal Aspects of Smart Home Activity with Circular Statistics. ECML/PKDD (2) 2015: 279-294 - [c75]Hao Song, Peter A. Flach:
Model Reuse with Subgroup Discovery. DC@PKDD/ECML 2015 - 2014
- [j31]Nanlin Jin, Peter A. Flach, Tom Wilcox, Royston Sellman, Joshua Thumim, Arno J. Knobbe:
Subgroup Discovery in Smart Electricity Meter Data. IEEE Trans. Ind. Informatics 10(2): 1327-1336 (2014) - [c74]Niall Twomey, Peter A. Flach:
A Machine Learning Approach to Objective Cardiac Event Detection. CISIS 2014: 519-524 - [c73]Reem Al-Otaibi, Meelis Kull, Peter A. Flach:
LaCova: A Tree-Based Multi-label Classifier Using Label Covariance as Splitting Criterion. ICMLA 2014: 74-79 - [c72]Meelis Kull, Peter A. Flach:
Reliability Maps: A Tool to Enhance Probability Estimates and Improve Classification Accuracy. ECML/PKDD (2) 2014: 18-33 - [c71]Louise A. C. Millard, Peter A. Flach, Julian P. T. Higgins:
Rate-Constrained Ranking and the Rate-Weighted AUC. ECML/PKDD (2) 2014: 386-403 - [c70]Louise A. C. Millard, Meelis Kull, Peter A. Flach:
Rate-Oriented Point-Wise Confidence Bounds for ROC Curves. ECML/PKDD (2) 2014: 404-421 - 2013
- [j30]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special section of selected papers from ECML-PKDD 2012. Data Min. Knowl. Discov. 27(3): 442-443 (2013) - [j29]Simon Price, Peter A. Flach, Sebastian Spiegler, Christopher Bailey, Nikki Rogers:
SubSift web services and workflows for profiling and comparing scientists and their published works. Future Gener. Comput. Syst. 29(2): 569-581 (2013) - [j28]Tijl De Bie, Peter A. Flach:
Guest editors' introduction: special issue of selected papers from ECML-PKDD 2012. Mach. Learn. 92(1): 1-3 (2013) - [j27]José Hernández-Orallo, Peter A. Flach, César Ferri:
ROC curves in cost space. Mach. Learn. 93(1): 71-91 (2013) - [c69]Simon Price, Peter A. Flach:
A Higher-order data flow model for heterogeneous Big Data. IEEE BigData 2013: 569-574 - 2012
- [b2]Peter A. Flach:
Machine Learning - The Art and Science of Algorithms that Make Sense of Data. Cambridge University Press 2012, ISBN 978-1-10-742222-3, pp. I-XVII, 1-396 - [j26]Daniel P. Berrar, Peter A. Flach:
Caveats and pitfalls of ROC analysis in clinical microarray research (and how to avoid them). Briefings Bioinform. 13(1): 83-97 (2012) - [j25]José Hernández-Orallo, Peter A. Flach, César Ferri:
A unified view of performance metrics: translating threshold choice into expected classification loss. J. Mach. Learn. Res. 13: 2813-2869 (2012) - [j24]Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan:
ILP turns 20 - Biography and future challenges. Mach. Learn. 86(1): 3-23 (2012) - [e6]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part I. Lecture Notes in Computer Science 7523, Springer 2012, ISBN 978-3-642-33459-7 [contents] - [e5]Peter A. Flach, Tijl De Bie, Nello Cristianini:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2012, Bristol, UK, September 24-28, 2012. Proceedings, Part II. Lecture Notes in Computer Science 7524, Springer 2012, ISBN 978-3-642-33485-6 [contents] - 2011
- [j23]Peter A. Flach:
The Machine Learning journal: 25 years young. Mach. Learn. 82(3): 273-274 (2011) - [c68]José Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez:
Brier Curves: a New Cost-Based Visualisation of Classifier Performance. ICML 2011: 585-592 - [c67]Peter A. Flach, José Hernández-Orallo, Cèsar Ferri Ramirez:
A Coherent Interpretation of AUC as a Measure of Aggregated Classification Performance. ICML 2011: 657-664 - [c66]William Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin:
Smooth Receiver Operating Characteristics (smROC) Curves. ECML/PKDD (2) 2011: 193-208 - [i5]José Hernández-Orallo, Peter A. Flach, Cèsar Ferri Ramirez:
Technical Note: Towards ROC Curves in Cost Space. CoRR abs/1107.5930 (2011) - [i4]Song Liu, Peter A. Flach, Nello Cristianini:
Generic Multiplicative Methods for Implementing Machine Learning Algorithms on MapReduce. CoRR abs/1111.2111 (2011) - [i3]José Hernández-Orallo, Peter A. Flach, César Ferri:
Threshold Choice Methods: the Missing Link. CoRR abs/1112.2640 (2011) - 2010
- [j22]Peter A. Flach:
The Machine Learning journal: 250 issues and counting. Mach. Learn. 81(3): 227-228 (2010) - [c65]Sebastian Spiegler, Peter A. Flach:
Enhanced Word Decomposition by Calibrating the Decision Threshold of Probabilistic Models and Using a Model Ensemble. ACL 2010: 375-383 - [c64]Sebastian Spiegler, Andrew van der Spuy, Peter A. Flach:
Ukwabelana - An open-source morphological Zulu corpus. COLING 2010: 1020-1028 - [c63]Simon Price, Peter A. Flach, Sebastian Spiegler, Christopher Bailey, Nikki Rogers:
SubSift Web Services and Workflows for Profiling and Comparing Scientists and Their Published Works. eScience 2010: 182-189 - [c62]Tarek Abudawood, Peter A. Flach:
The Advantages of Seed Examples in First-Order Multi-class Subgroup Discovery. ECAI 2010: 1113-1114 - [c61]Tarek Abudawood, Peter A. Flach:
Learning Multi-class Theories in ILP. ILP 2010: 6-13 - [c60]Tarek Abudawood, Peter A. Flach:
First-Order Multi-class Subgroup Discovery. STAIRS 2010: 1-12 - [c59]Simon Price, Peter A. Flach, Sebastian Spiegler:
SubSift: a novel application of the vector space model to support the academic research process. WAPA 2010: 20-27 - [c58]Tarek Abudawood, Peter A. Flach:
Exploiting the High Predictive Power of Multi-class Subgroups. ACML 2010: 177-192 - [r2]Peter A. Flach:
First-Order Logic. Encyclopedia of Machine Learning 2010: 410-415 - [r1]Peter A. Flach:
ROC Analysis. Encyclopedia of Machine Learning 2010: 869-875
2000 – 2009
- 2009
- [j21]Antonis C. Kakas, Peter A. Flach:
Abduction and Induction in Artificial Intelligence. J. Appl. Log. 7(3): 251 (2009) - [j20]Peter A. Flach, Sebastian Spiegler, Bruno Golénia, Simon Price, John Guiver, Ralf Herbrich, Thore Graepel, Mohammed J. Zaki:
Novel tools to streamline the conference review process: experiences from SIGKDD'09. SIGKDD Explor. 11(2): 63-67 (2009) - [j19]Kseniya B. Shalonova, Bruno Golénia, Peter A. Flach:
Towards Learning Morphology for Under-Resourced Fusional and Agglutinating Languages. IEEE Trans. Speech Audio Process. 17(5): 956-965 (2009) - [c57]William Klement, Peter A. Flach, Nathalie Japkowicz, Stan Matwin:
Cost-Based Sampling of Individual Instances. Canadian AI 2009: 86-97 - [c56]Sebastian Spiegler, Bruno Golénia, Peter A. Flach:
Unsupervised Word Decomposition with the Promodes Algorithm. CLEF (1) 2009: 625-632 - [c55]Bruno Golénia, Sebastian Spiegler, Peter A. Flach:
Unsupervised Morpheme Discovery with Ungrade. CLEF (1) 2009: 633-640 - [c54]Bruno Golénia, Sebastian Spiegler, Peter A. Flach:
UNGRADE: UNsupervised GRAph DEcomposition. CLEF (Working Notes) 2009 - [c53]Sebastian Spiegler, Bruno Golénia, Peter A. Flach:
PROMODES: A Probabilistic Generative Model for Word Decomposition. CLEF (Working Notes) 2009 - [c52]Susanne Hoche, David Hardcastle, Peter A. Flach:
Using Time Dependent Link Reduction to Improve the Efficiency of Topic Prediction in Co-Authorship Graphs. CompleNet 2009: 173-184 - [c51]Tarek Abudawood, Peter A. Flach:
Evaluation Measures for Multi-class Subgroup Discovery. ECML/PKDD (1) 2009: 35-50 - [e4]John F. Elder IV, Françoise Fogelman-Soulié, Peter A. Flach, Mohammed Javeed Zaki:
Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28 - July 1, 2009. ACM 2009, ISBN 978-1-60558-495-9 [contents] - 2008
- [c50]Susanne Hoche, Peter A. Flach, David Hardcastle:
A Fast Method for Property Prediction in Graph-Structured Data from Positive and Unlabelled Examples. ECAI 2008: 162-166 - [c49]Simon Price, Peter A. Flach:
Querying and Merging Heterogeneous Data by Approximate Joins on Higher-Order Terms. ILP 2008: 226-243 - [c48]Sebastian Spiegler, Bruno Golénia, Ksenia Shalonova, Peter A. Flach, Roger C. F. Tucker:
Learning the morphology of Zulu with different degrees of supervision. SLT 2008: 9-12 - 2007
- [j18]Susanne Hoche, Andreas Nürnberger, Peter A. Flach:
Network analysis in natural sciences and engineering. AI Commun. 20(4): 229-230 (2007) - [c47]Shaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez:
An Improved Model Selection Heuristic for AUC. ECML 2007: 478-489 - [c46]Peter A. Flach, Edson Takashi Matsubara:
A Simple Lexicographic Ranker and Probability Estimator. ECML 2007: 575-582 - [c45]Peter A. Flach:
Putting Things in Order: On the Fundamental Role of Ranking in Classification and Probability Estimation. ECML/PKDD 2007: 2-3 - [i2]Peter A. Flach, Edson Takashi Matsubara:
On classification, ranking, and probability estimation. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [c44]Peter A. Flach:
Reinventing Machine Learning with ROC Analysis. IBERAMIA-SBIA 2006: 4-5 - [c43]Kerstin Eder, Peter A. Flach, Hsiou-Wen Hsueh:
Towards Automating Simulation-Based Design Verification Using ILP. ILP 2006: 154-168 - 2005
- [j17]Tom Fawcett, Peter A. Flach:
A Response to Webb and Ting's On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions. Mach. Learn. 58(1): 33-38 (2005) - [j16]Johannes Fürnkranz, Peter A. Flach:
ROC 'n' Rule Learning - Towards a Better Understanding of Covering Algorithms. Mach. Learn. 58(1): 39-77 (2005) - [c42]Elias Gyftodimos, Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations. IDA 2005: 145-156 - [c41]Peter A. Flach, Shaomin Wu:
Repairing Concavities in ROC Curves. IJCAI 2005: 702-707 - [c40]Ronaldo C. Prati, Peter A. Flach:
ROCCER: An Algorithm for Rule Learning Based on ROC Analysis. IJCAI 2005: 823-828 - [i1]Elias Gyftodimos, Peter A. Flach:
Combining Bayesian Networks with Higher-Order Data Representations. Probabilistic, Logical and Relational Learning 2005 - 2004
- [j15]Nada Lavrac, Branko Kavsek, Peter A. Flach, Ljupco Todorovski:
Subgroup Discovery with CN2-SD. J. Mach. Learn. Res. 5: 153-188 (2004) - [j14]Nada Lavrac, Bojan Cestnik, Dragan Gamberger, Peter A. Flach:
Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned. Mach. Learn. 57(1-2): 115-143 (2004) - [j13]Thomas Gärtner, John W. Lloyd, Peter A. Flach:
Kernels and Distances for Structured Data. Mach. Learn. 57(3): 205-232 (2004) - [j12]Peter A. Flach, Nicolas Lachiche:
Naive Bayesian Classification of Structured Data. Mach. Learn. 57(3): 233-269 (2004) - [j11]José Hernández-Orallo, César Ferri, Nicolas Lachiche, Peter A. Flach:
The 1st workshop on ROC analysis in artificial intelligence (ROCAI-2004). SIGKDD Explor. 6(2): 159-161 (2004) - [j10]Peter A. Flach:
Book review: Logic for Learning: Learning Comprehensible Theories from Structured Data by John W. Lloyd, Springer-Verlag, 2003, ISBN 3-540-42027-4. Theory Pract. Log. Program. 4(5-6): 753-755 (2004) - [c39]Johannes Fürnkranz, Peter A. Flach:
An Analysis of Stopping and Filtering Criteria for Rule Learning. ECML 2004: 123-133 - [c38]Annalisa Appice, Michelangelo Ceci, Simon Alan Rawles, Peter A. Flach:
Redundant feature elimination for multi-class problems. ICML 2004 - [c37]César Ferri, Peter A. Flach, José Hernández-Orallo:
Delegating classifiers. ICML 2004 - [c36]Elias Gyftodimos, Peter A. Flach:
Hierarchical Bayesian Networks: An Approach to Classification and Learning for Structured Data. SETN 2004: 291-300 - [e3]José Hernández-Orallo, César Ferri, Nicolas Lachiche, Peter A. Flach:
ROC Analysis in Artificial Intelligence, 1st International Workshop, ROCAI-2004, Valencia, Spain, August 22, 2004. 2004 [contents] - 2003
- [c35]Thomas Gärtner, Peter A. Flach, Stefan Wrobel:
On Graph Kernels: Hardness Results and Efficient Alternatives. COLT 2003: 129-143 - [c34]César Ferri, Peter A. Flach, José Hernández-Orallo:
Improving the AUC of Probabilistic Estimation Trees. ECML 2003: 121-132 - [c33]Peter A. Flach:
The Geometry of ROC Space: Understanding Machine Learning Metrics through ROC Isometrics. ICML 2003: 194-201 - [c32]Johannes Fürnkranz, Peter A. Flach:
An Analysis of Rule Evaluation Metrics. ICML 2003: 202-209 - [c31]Nicolas Lachiche, Peter A. Flach:
Improving Accuracy and Cost of Two-class and Multi-class Probabilistic Classifiers Using ROC Curves. ICML 2003: 416-423 - [c30]Mark-A. Krogel, Simon Alan Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel:
Comparative Evaluation of Approaches to Propositionalization. ILP 2003: 197-214 - [c29]Dimitrios Mavroeidis, Peter A. Flach:
Improved Distances for Structured Data. ILP 2003: 251-268 - 2002
- [j9]Tanja Urbancic, Maja Skrjanc, Peter A. Flach:
Web-based analysis of data mining and decision support education. AI Commun. 15(4): 199-204 (2002) - [c28]Peter A. Flach, Nada Lavrac:
Learning in Clausal Logic: A Perspective on Inductive Logic Programming. Computational Logic: Logic Programming and Beyond 2002: 437-471 - [c27]Yonghong Peng, Peter A. Flach, Carlos Soares, Pavel Brazdil:
Improved Dataset Characterisation for Meta-learning. Discovery Science 2002: 141-152 - [c26]Nada Lavrac, Peter A. Flach, Branko Kavsek, Ljupco Todorovski:
Adapting classification rule induction to subgroup discovery. ICDM 2002: 266-273 - [c25]César Ferri, Peter A. Flach, José Hernández-Orallo:
Learning Decision Trees Using the Area Under the ROC Curve. ICML 2002: 139-146 - [c24]Thomas Gärtner, Peter A. Flach, Adam Kowalczyk, Alexander J. Smola:
Multi-Instance Kernels. ICML 2002: 179-186 - [c23]Thomas Gärtner, John W. Lloyd, Peter A. Flach:
Kernels for Structured Data. ILP 2002: 66-83 - [c22]Nicolas Lachiche, Peter A. Flach:
1BC2: A True First-Order Bayesian Classifier. ILP 2002: 133-148 - [c21]Nada Lavrac, Filip Zelezný, Peter A. Flach:
RSD: Relational Subgroup Discovery through First-Order Feature Construction. ILP 2002: 149-165 - 2001
- [j8]Peter A. Flach:
On the state of the art in machine learning: A personal review. Artif. Intell. 131(1-2): 199-222 (2001) - [j7]Peter A. Flach, Nicolas Lachiche:
Confirmation-Guided Discovery of First-Order Rules with Tertius. Mach. Learn. 42(1/2): 61-95 (2001) - [j6]Peter A. Flach, Saso Dzeroski:
Editorial: Inductive Logic Programming is Coming of Age. Mach. Learn. 44(3): 207-209 (2001) - [j5]Nada Lavrac, Peter A. Flach:
An extended transformation approach to inductive logic programming. ACM Trans. Comput. Log. 2(4): 458-494 (2001) - [c20]Peter A. Flach:
Multi-relational Data Mining: a perspective. EPIA 2001: 3-4 - [c19]Thomas Gärtner, Peter A. Flach:
WBCsvm: Weighted Bayesian Classification based on Support Vector Machines. ICML 2001: 154-161 - [e2]Luc De Raedt, Peter A. Flach:
Machine Learning: EMCL 2001, 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001, Proceedings. Lecture Notes in Computer Science 2167, Springer 2001, ISBN 3-540-42536-5 [contents] - 2000
- [j4]Iztok Savnik, Peter A. Flach:
Discovery of multivalued dependencies from relations. Intell. Data Anal. 4(3-4): 195-211 (2000) - [c18]Peter A. Flach, Nicolas Lachiche:
Decomposing Probability Distributions on Structured Individuals. ILP Work-in-progress reports 2000 - [c17]Ljupco Todorovski, Peter A. Flach, Nada Lavrac:
Predictive Performance of Weghted Relative Accuracy. PKDD 2000: 255-264 - [c16]Peter A. Flach:
The Use of Functional and Logic Languages in Machine Learning. WFLP 2000: 225-237
1990 – 1999
- 1999
- [j3]Peter A. Flach, Iztok Savnik:
Database Dependency Discovery: A Machine Learning Approach. AI Commun. 12(3): 139-160 (1999) - [c15]Peter A. Flach:
Knowledge Representation for Inductive Learning. ESCQARU 1999: 160-167 - [c14]Peter A. Flach, Nicolas Lachiche:
IBC: A First-Order Bayesian Classifier. ILP 1999: 92-103 - [c13]Nada Lavrac, Peter A. Flach, Blaz Zupan:
Rule Evaluation Measures: A Unifying View. ILP 1999: 174-185 - [e1]Saso Dzeroski, Peter A. Flach:
Inductive Logic Programming, 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999, Proceedings. Lecture Notes in Computer Science 1634, Springer 1999, ISBN 3-540-66109-3 [contents] - 1998
- [j2]Peter A. Flach, Antonis C. Kakas, Ruy J. G. B. de Queiroz, Katia Silva Guimarães:
Conference Report: Abduction and Induction in AI; Logic, Proofs and Algorithms; Logic in Natural Language; Logic for Concurrency and Synchronisation (LOCUS). Log. J. IGPL 6(4): 651-663 (1998) - [c12]Peter A. Flach:
From Extensional to Intensional Knowledge: Inductive Logic Programming Techniques and Their Application to Deductive Databases. Transactions and Change in Logic Databases 1998: 356-387 - [c11]Peter A. Flach, Christophe G. Giraud-Carrier, John W. Lloyd:
Strongly Typed Inductive Concept Learning. ILP 1998: 185-194 - [c10]Peter A. Flach:
Comparing Consequence Relations. KR 1998: 180-189 - 1997
- [j1]Peter A. Flach, Antonis C. Kakas:
Abductive and Inductive Reasoning: Report of the ECAI'96 Workshop. Log. J. IGPL 5(5): 773-778 (1997) - [c9]Peter A. Flach:
Inductive Logic Databases: From Extensional to Intensional Knowledge. DOOD 1997: 3 - [c8]Peter A. Flach:
Normal Forms for Inductive Logic Programming. ILP 1997: 149-156 - 1996
- [c7]Peter A. Flach:
Rationality Postulates for Induction. TARK 1996: 267-281 - 1994
- [b1]Peter A. Flach:
Simply logical - intelligent reasoning by example. Wiley professional computing, Wiley 1994, ISBN 978-0-471-94152-1, pp. I-XV, 1-240 - 1993
- [c6]Peter A. Flach:
Predicate Invention in Inductive Data Engineering. ECML 1993: 83-94 - 1992
- [c5]Peter A. Flach:
An Analysis of Various Forms of "Jumping to Conclusions". AII 1992: 170-186 - [c4]Peter A. Flach:
A Model of Inductive Reasoning. Logic at Work 1992: 41-56 - 1991
- [c3]Shan-Hwei Nienhuys-Cheng, Peter A. Flach:
Consistent Term Mappings, Term Partitions and Inverse Resolution. EWSL 1991: 361-374 - [c2]Peter A. Flach:
Towards a Theory of Inductive Logic Programming. ISMIS 1991: 510-519
1980 – 1989
- 1989
- [c1]Peter A. Flach:
Second-order Inductive Learning. AII 1989: 202-216
Coauthor Index
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