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2020 – today
- 2023
[j63]Lukasz Korycki, Bartosz Krawczyk
:
Adversarial concept drift detection under poisoning attacks for robust data stream mining. Mach. Learn. 112(10): 4013-4048 (2023)- [j62]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. IEEE Trans. Neural Networks Learn. Syst. 34(9): 6390-6404 (2023) - [c101]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. ICDE 2023: 1433-1446 - [i18]Lukasz Korycki, Bartosz Krawczyk:
Class-Incremental Mixture of Gaussians for Deep Continual Learning. CoRR abs/2307.04094 (2023) - 2022
- [j61]Alberto Cano, Bartosz Krawczyk:
ROSE: robust online self-adjusting ensemble for continual learning on imbalanced drifting data streams. Mach. Learn. 111(7): 2561-2599 (2022) - [j60]Lukasz Korycki, Bartosz Krawczyk:
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams. Pattern Recognit. 129: 108749 (2022) - [i17]Gabriel Aguiar, Bartosz Krawczyk, Alberto Cano:
A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework. CoRR abs/2204.03719 (2022) - [i16]Damien Dablain, Colin Bellinger, Bartosz Krawczyk, Nitesh V. Chawla:
Efficient Augmentation for Imbalanced Deep Learning. CoRR abs/2207.06080 (2022) - [i15]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learning. CoRR abs/2207.06084 (2022) - [i14]Damien A. Dablain, Colin Bellinger, Bartosz Krawczyk, David W. Aha, Nitesh V. Chawla:
Interpretable ML for Imbalanced Data. CoRR abs/2212.07743 (2022) - 2021
- [j59]Sina Ghadermarzi, Bartosz Krawczyk, Jiangning Song, Lukasz A. Kurgan:
XRRpred: accurate predictor of crystal structure quality from protein sequence. Bioinform. 37(23): 4366-4374 (2021) - [j58]Martha Roseberry, Bartosz Krawczyk, Youcef Djenouri, Alberto Cano:
Self-adjusting k nearest neighbors for continual learning from multi-label drifting data streams. Neurocomputing 442: 10-25 (2021) - [j57]William C. Sleeman IV, Bartosz Krawczyk:
Multi-class imbalanced big data classification on Spark. Knowl. Based Syst. 212: 106598 (2021) - [j56]Bartosz Krawczyk:
Tensor decision trees for continual learning from drifting data streams. Mach. Learn. 110(11): 3015-3035 (2021) - [c100]Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz:
On the combined effect of class imbalance and concept complexity in deep learning. IEEE BigData 2021: 4859-4868 - [c99]Lukasz Korycki, Bartosz Krawczyk:
Class-Incremental Experience Replay for Continual Learning Under Concept Drift. CVPR Workshops 2021: 3649-3658 - [c98]Bartosz Krawczyk:
Tensor Decision Trees for Continual Learning from Drifting Data Streams. DSAA 2021: 1-2 - [c97]Lukasz Korycki, Bartosz Krawczyk:
Concept Drift Detection from Multi-Class Imbalanced Data Streams. ICDE 2021: 1068-1079 - [c96]Bartosz Krawczyk, Colin Bellinger, Roberto Corizzo, Nathalie Japkowicz:
Undersampling with Support Vectors for Multi-Class Imbalanced Data Classification. IJCNN 2021: 1-7 - [c95]Filip Guzy, Michal Wozniak, Bartosz Krawczyk:
Evaluating and Explaining Generative Adversarial Networks for Continual Learning under Concept Drift. ICDM (Workshops) 2021: 295-303 - [c94]Bartosz Krawczyk, Alberto Cano:
Locally Linear Support Vector Machines for Imbalanced Data Classification. PAKDD (1) 2021: 616-628 - [c93]Lukasz Korycki, Bartosz Krawczyk:
Low-Dimensional Representation Learning from Imbalanced Data Streams. PAKDD (1) 2021: 629-641 - [c92]Lukasz Korycki, Bartosz Krawczyk:
Streaming Decision Trees for Lifelong Learning. ECML/PKDD (1) 2021: 502-518 - [i13]Lukasz Korycki, Bartosz Krawczyk:
Concept Drift Detection from Multi-Class Imbalanced Data Streams. CoRR abs/2104.10228 (2021) - [i12]Lukasz Korycki, Bartosz Krawczyk:
Class-Incremental Experience Replay for Continual Learning under Concept Drift. CoRR abs/2104.11861 (2021) - [i11]Damien Dablain, Bartosz Krawczyk, Nitesh V. Chawla:
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data. CoRR abs/2105.02340 (2021) - [i10]William C. Sleeman IV, Bartosz Krawczyk:
Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions. CoRR abs/2107.11508 (2021) - [i9]Kushankur Ghosh, Colin Bellinger, Roberto Corizzo, Bartosz Krawczyk, Nathalie Japkowicz:
On the combined effect of class imbalance and concept complexity in deep learning. CoRR abs/2107.14194 (2021) - [i8]Lukasz Korycki, Bartosz Krawczyk:
Mining Drifting Data Streams on a Budget: Combining Active Learning with Self-Labeling. CoRR abs/2112.11019 (2021) - 2020
- [j55]William C. Sleeman IV, Joseph Nalluri, Khajamoinuddin Syed, Preetam Ghosh, Bartosz Krawczyk, Michael Hagan, Jatinder Palta, Rishabh Kapoor:
A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels. J. Biomed. Informatics 109: 103527 (2020) - [j54]Michal Koziarski, Michal Wozniak, Bartosz Krawczyk:
Combined Cleaning and Resampling algorithm for multi-class imbalanced data with label noise. Knowl. Based Syst. 204: 106223 (2020) - [j53]Alberto Cano, Bartosz Krawczyk:
Kappa Updated Ensemble for drifting data stream mining. Mach. Learn. 109(1): 175-218 (2020) - [j52]Bartosz Krawczyk, Michal Koziarski, Michal Wozniak:
Radial-Based Oversampling for Multiclass Imbalanced Data Classification. IEEE Trans. Neural Networks Learn. Syst. 31(8): 2818-2831 (2020) - [c91]Lukasz Korycki, Bartosz Krawczyk:
Online Oversampling for Sparsely Labeled Imbalanced and Non-Stationary Data Streams. IJCNN 2020: 1-8 - [i7]Michal Koziarski, Michal Wozniak, Bartosz Krawczyk:
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise. CoRR abs/2004.03406 (2020) - [i6]Lukasz Korycki, Bartosz Krawczyk:
Instance exploitation for learning temporary concepts from sparsely labeled drifting data streams. CoRR abs/2009.09382 (2020) - [i5]Lukasz Korycki, Bartosz Krawczyk:
Adversarial Concept Drift Detection under Poisoning Attacks for Robust Data Stream Mining. CoRR abs/2009.09497 (2020) - [i4]Lukasz Korycki, Bartosz Krawczyk:
Adaptive Deep Forest for Online Learning from Drifting Data Streams. CoRR abs/2010.07340 (2020)
2010 – 2019
- 2019
- [j51]José A. Sáez, Mikel Galar, Bartosz Krawczyk:
Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy. IEEE Access 7: 83396-83411 (2019) - [j50]Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera:
Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation. Expert Syst. Appl. 118: 315-328 (2019) - [j49]José Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michal Wozniak, Salvador García:
Monotonic classification: An overview on algorithms, performance measures and data sets. Neurocomputing 341: 168-182 (2019) - [j48]Michal Koziarski, Bartosz Krawczyk, Michal Wozniak:
Radial-Based oversampling for noisy imbalanced data classification. Neurocomputing 343: 19-33 (2019) - [j47]Przemyslaw Skryjomski, Bartosz Krawczyk, Alberto Cano:
Speeding up k-Nearest Neighbors classifier for large-scale multi-label learning on GPUs. Neurocomputing 354: 10-19 (2019) - [j46]Bartosz Krawczyk, Isaac Triguero, Salvador García, Michal Wozniak, Francisco Herrera:
Instance reduction for one-class classification. Knowl. Inf. Syst. 59(3): 601-628 (2019) - [j45]Alberto Cano, Bartosz Krawczyk:
Evolving rule-based classifiers with genetic programming on GPUs for drifting data streams. Pattern Recognit. 87: 248-268 (2019) - [j44]Martha Roseberry, Bartosz Krawczyk, Alberto Cano:
Multi-Label Punitive kNN with Self-Adjusting Memory for Drifting Data Streams. ACM Trans. Knowl. Discov. Data 13(6): 60:1-60:31 (2019) - [c90]Lukasz Korycki, Alberto Cano, Bartosz Krawczyk:
Active Learning with Abstaining Classifiers for Imbalanced Drifting Data Streams. IEEE BigData 2019: 2334-2343 - [c89]William C. Sleeman IV, Bartosz Krawczyk:
Bagging Using Instance-Level Difficulty for Multi-Class Imbalanced Big Data Classification on Spark. IEEE BigData 2019: 2484-2493 - [c88]Lukasz Korycki, Bartosz Krawczyk:
Unsupervised Drift Detector Ensembles for Data Stream Mining. DSAA 2019: 317-325 - [c87]Bartosz Krawczyk, Michal Wozniak:
On the Role of Cost-Sensitive Learning in Imbalanced Data Oversampling. ICCS (3) 2019: 180-191 - [c86]Bartosz Krawczyk, Alberto Cano:
Adaptive Ensemble Active Learning for Drifting Data Stream Mining. IJCAI 2019: 2763-2771 - [i3]Krzysztof J. Cios, Bartosz Krawczyk, Jacquelyne Cios, Kevin J. Staley:
Uniqueness of Medical Data Mining: How the new technologies and data they generate are transforming medicine. CoRR abs/1905.09203 (2019) - 2018
- [b1]Alberto Fernández, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk, Francisco Herrera:
Learning from Imbalanced Data Sets. Springer 2018, ISBN 978-3-319-98073-7, pp. 1-377 - [j43]Bartosz Krawczyk, Alberto Cano:
Online ensemble learning with abstaining classifiers for drifting and noisy data streams. Appl. Soft Comput. 68: 677-692 (2018) - [j42]Pawel Ksieniewicz, Bartosz Krawczyk, Michal Wozniak:
Ensemble of Extreme Learning Machines with trained classifier combination and statistical features for hyperspectral data. Neurocomputing 271: 28-37 (2018) - [j41]Bartosz Krawczyk, Bridget T. McInnes:
Local ensemble learning from imbalanced and noisy data for word sense disambiguation. Pattern Recognit. 78: 103-119 (2018) - [j40]Bartosz Krawczyk, Mikel Galar, Michal Wozniak, Humberto Bustince, Francisco Herrera:
Dynamic ensemble selection for multi-class classification with one-class classifiers. Pattern Recognit. 83: 34-51 (2018) - [c85]Lukasz Korycki, Bartosz Krawczyk:
Clustering-Driven and Dynamically Diversified Ensemble for Drifting Data Streams. IEEE BigData 2018: 1037-1044 - [c84]Bartosz Krawczyk, Bernhard Pfahringer, Michal Wozniak:
Combining active learning with concept drift detection for data stream mining. IEEE BigData 2018: 2239-2244 - [c83]Alberto Cano, Bartosz Krawczyk:
Learning Classification Rules with Differential Evolution for High-Speed Data Stream Mining on GPU s. CEC 2018: 1-8 - [c82]Andrzej Lapinski, Bartosz Krawczyk, Pawel Ksieniewicz, Michal Wozniak:
An Empirical Insight Into Concept Drift Detectors Ensemble Strategies. CEC 2018: 1-8 - [c81]Andriy Mulyar, Bartosz Krawczyk:
Addressing Local Class Imbalance in Balanced Datasets with Dynamic Impurity Decision Trees. DS 2018: 3-17 - [c80]José A. Sáez, Héctor Quintián, Bartosz Krawczyk, Michal Wozniak, Emilio Corchado:
Multi-class Imbalanced Data Oversampling for Vertebral Column Pathologies Classification. HAIS 2018: 131-142 - [c79]Shiven Sharma, Colin Bellinger, Bartosz Krawczyk, Osmar R. Zaïane, Nathalie Japkowicz:
Synthetic Oversampling with the Majority Class: A New Perspective on Handling Extreme Imbalance. ICDM 2018: 447-456 - [c78]Bartosz Krawczyk, Alberto Cano, Michal Wozniak:
Selecting local ensembles for multi-class imbalanced data classification. IJCNN 2018: 1-8 - [c77]Luís Torgo, Stan Matwin, Nathalie Japkowicz, Bartosz Krawczyk, Nuno Moniz, Paula Branco:
2nd Workshop on Learning with Imbalanced Domains: Preface. LIDTA@ECML/PKDD 2018: 1-7 - [c76]Bartosz Krawczyk, Michal Wozniak:
Leveraging Ensemble Pruning for Imbalanced Data Classification. SMC 2018: 439-444 - [i2]Anabel Gómez-Ríos, Siham Tabik, Julián Luengo, A. S. M. Shihavuddin, Bartosz Krawczyk, Francisco Herrera:
Towards Highly Accurate Coral Texture Images Classification Using Deep Convolutional Neural Networks and Data Augmentation. CoRR abs/1804.00516 (2018) - [i1]José Ramón Cano, Pedro Antonio Gutiérrez, Bartosz Krawczyk, Michal Wozniak, Salvador García:
Monotonic classification: an overview on algorithms, performance measures and data sets. CoRR abs/1811.07155 (2018) - 2017
- [j39]Jerzy Kowalski, Bartosz Krawczyk, Michal Wozniak:
Fault diagnosis of marine 4-stroke diesel engines using a one-vs-one extreme learning ensemble. Eng. Appl. Artif. Intell. 57: 134-141 (2017) - [j38]Sergio Ramírez-Gallego, Bartosz Krawczyk, Salvador García, Michal Wozniak, Francisco Herrera:
A survey on data preprocessing for data stream mining: Current status and future directions. Neurocomputing 239: 39-57 (2017) - [j37]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) - [j36]Bartosz Krawczyk:
Active and adaptive ensemble learning for online activity recognition from data streams. Knowl. Based Syst. 138: 69-78 (2017) - [j35]Bartosz Krawczyk, Boguslaw Cyganek:
Selecting locally specialised classifiers for one-class classification ensembles. Pattern Anal. Appl. 20(2): 427-439 (2017) - [j34]Michal Koziarski, Bartosz Krawczyk, Michal Wozniak:
The deterministic subspace method for constructing classifier ensembles. Pattern Anal. Appl. 20(4): 981-990 (2017) - [j33]Sergio Ramírez-Gallego, Bartosz Krawczyk, Salvador García, Michal Wozniak, José Manuel Benítez, Francisco Herrera:
Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark. IEEE Trans. Syst. Man Cybern. Syst. 47(10): 2727-2739 (2017) - [c75]Lukasz Korycki, Bartosz Krawczyk:
Combining Active Learning and Self-Labeling for Data Stream Mining. CORES 2017: 481-490 - [c74]Bartosz Krawczyk, Bridget T. McInnes, Alberto Cano:
Sentiment Classification from Multi-class Imbalanced Twitter Data Using Binarization. HAIS 2017: 26-37 - [c73]Michal Koziarski, Bartosz Krawczyk, Michal Wozniak:
Radial-Based Approach to Imbalanced Data Oversampling. HAIS 2017: 318-327 - [c72]Gerald Schaefer, Mateusz Budnik, Bartosz Krawczyk:
Immersive browsing in an image sphere. IMCOM 2017: 26 - [c71]Bartosz Krawczyk, Michal Wozniak:
Online query by committee for active learning from drifting data streams. IJCNN 2017: 2120-2127 - [c70]Luís Torgo, Bartosz Krawczyk, Paula Branco, Nuno Moniz:
Learning with Imbalanced Domains: Preface. LIDTA@PKDD/ECML 2017: 1-6 - [c69]Przemyslaw Skryjomski, Bartosz Krawczyk:
Influence of minority class instance types on SMOTE imbalanced data oversampling. LIDTA@PKDD/ECML 2017: 7-21 - [c68]Bartosz Krawczyk, Przemyslaw Skryjomski:
Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams. ECML/PKDD (2) 2017: 512-527 - 2016
- [j32]Boguslaw Cyganek, Manuel Graña, Bartosz Krawczyk, Andrzej Kasprzak, Piotr Porwik, Krzysztof Walkowiak, Michal Wozniak:
A Survey of Big Data Issues in Electronic Health Record Analysis. Appl. Artif. Intell. 30(6): 497-520 (2016) - [j31]José A. Sáez, Bartosz Krawczyk, Michal Wozniak:
On the Influence of Class Noise in Medical Data Classification: Treatment Using Noise Filtering Methods. Appl. Artif. Intell. 30(6): 590-609 (2016) - [j30]Bartosz Krawczyk, Mikel Galar, Lukasz Jelen, Francisco Herrera:
Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Appl. Soft Comput. 38: 714-726 (2016) - [j29]Bartosz Krawczyk, Michal Wozniak:
Untrained weighted classifier combination with embedded ensemble pruning. Neurocomputing 196: 14-22 (2016) - [j28]Zhongliang Zhang, Bartosz Krawczyk, Salvador García, Alejandro Rosales-Pérez, Francisco Herrera:
Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data. Knowl. Based Syst. 106: 251-263 (2016) - [j27]Bartosz Krawczyk, Michal Wozniak:
Dynamic classifier selection for one-class classification. Knowl. Based Syst. 107: 43-53 (2016) - [j26]Bartosz Krawczyk:
Learning from imbalanced data: open challenges and future directions. Prog. Artif. Intell. 5(4): 221-232 (2016) - [j25]José A. Sáez, Bartosz Krawczyk, Michal Wozniak:
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets. Pattern Recognit. 57: 164-178 (2016) - [c67]Bartosz Krawczyk:
Hybrid One-Class Ensemble for High-Dimensional Data Classification. ACIIDS (2) 2016: 136-144 - [c66]Pawel Ksieniewicz, Bartosz Krawczyk, Michal Wozniak:
Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis. DMBD 2016: 513-520 - [c65]Michal Koziarski, Bartosz Krawczyk, Michal Wozniak:
Forming Classifier Ensembles with Deterministic Feature Subspaces. FedCSIS 2016: 89-95 - [c64]Bartosz Krawczyk, José A. Sáez, Michal Wozniak:
Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines. FUZZ-IEEE 2016: 915-922 - [c63]Michal Wozniak, Bartosz Krawczyk:
Workshop on Nonstationary Models of Pattern Recognition and Classifier Combinations. ICCS 2016: 1670 - [c62]Bartosz Krawczyk:
GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift. ICCS 2016: 1692-1701 - [c61]Bartosz Krawczyk:
Cost-sensitive one-vs-one ensemble for multi-class imbalanced data. IJCNN 2016: 2447-2452 - 2015
- [j24]Bartosz Krawczyk, Gerald Schaefer, Michal Wozniak:
A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification. Artif. Intell. Medicine 65(3): 219-227 (2015) - [j23]Boguslaw Cyganek, Bartosz Krawczyk, Michal Wozniak:
Multidimensional data classification with chordal distance based kernel and Support Vector Machines. Eng. Appl. Artif. Intell. 46: 10-22 (2015) - [j22]Bartosz Krawczyk, Jerzy Stefanowski, Michal Wozniak:
Data stream classification and big data analytics. Neurocomputing 150: 238-239 (2015) - [j21]Bartosz Krawczyk:
One-class classifier ensemble pruning and weighting with firefly algorithm. Neurocomputing 150: 490-500 (2015) - [j20]Bartosz Krawczyk, Michal Wozniak:
Incremental weighted one-class classifier for mining stationary data streams. J. Comput. Sci. 9: 19-25 (2015) - [j19]Bartosz Krawczyk, Bogdan Trawinski:
Hybrid Ensemble Machine Learning for Complex and Dynamic Data. New Gener. Comput. 33(4): 341-344 (2015) - [j18]Bartosz Krawczyk:
Forming Ensembles of Soft One-Class Classifiers with Weighted Bagging. New Gener. Comput. 33(4): 449-466 (2015) - [j17]Bartosz Krawczyk, Michal Wozniak, Francisco Herrera:
On the usefulness of one-class classifier ensembles for decomposition of multi-class problems. Pattern Recognit. 48(12): 3969-3982 (2015) - [j16]Bartosz Krawczyk, Michal Wozniak:
One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput. 19(12): 3387-3400 (2015) - [c60]Bartosz Krawczyk, Michal Wozniak:
Pruning Ensembles of One-Class Classifiers with X-means Clustering. ACIIDS (1) 2015: 484-493 - [c59]Bartosz Krawczyk, Michal Wozniak:
Pruning Ensembles with Cost Constraints. ACIIDS (1) 2015: 503-512 - [c58]Boguslaw Cyganek, Bartosz Krawczyk:
Data Classification with Ensembles of One-Class Support Vector Machines and Sparse Nonnegative Matrix Factorization. ACIIDS (1) 2015: 526-535 - [c57]Bartosz Krawczyk:
Combining One-vs-One Decomposition and Ensemble Learning for Multi-class Imbalanced Data. CORES 2015: 27-36 - [c56]Bartosz Krawczyk, Michal Wozniak:
Reacting to different types of concept drift with adaptive and incremental one-class classifiers. CYBCONF 2015: 30-35 - [c55]Bartosz Krawczyk, Michal Wozniak:
Combining nearest neighbour classifiers based on small subsamples for big data analytics. CYBCONF 2015: 311-316 - [c54]Bartosz Krawczyk, Michal Wozniak:
Wagging for Combining Weighted One-class Support Vector Machines. ICCS 2015: 1565-1573 - [c53]Bartosz Krawczyk, Michal Wozniak:
Cost-Sensitive Neural Network with ROC-Based Moving Threshold for Imbalanced Classification. IDEAL 2015: 45-52 - [c52]