Learning from imbalanced data: open challenges and future directions B Krawczyk Progress in Artificial Intelligence 5 (4), 221-232, 2016 | 2290 | 2016 |
Learning from imbalanced data sets A Fernández, S García, M Galar, RC Prati, B Krawczyk, F Herrera Springer 10 (2018), 2018 | 1449* | 2018 |
Ensemble learning for data stream analysis: A survey B Krawczyk, LL Minku, J Gama, J Stefanowski, M Woźniak Information Fusion 37, 132-156, 2017 | 1056 | 2017 |
A survey on data preprocessing for data stream mining: Current status and future directions S Ramírez-Gallego, B Krawczyk, S García, M Woźniak, F Herrera Neurocomputing 239, 39-57, 2017 | 523 | 2017 |
Cost-sensitive decision tree ensembles for effective imbalanced classification B Krawczyk, M Woźniak, G Schaefer Applied Soft Computing 14, 554-562, 2014 | 394 | 2014 |
Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy B Krawczyk, M Galar, Ł Jeleń, F Herrera Applied Soft Computing 38, 714-726, 2016 | 277 | 2016 |
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets JA Sáez, B Krawczyk, M Woźniak Pattern Recognition 57, 164-178, 2016 | 250 | 2016 |
DeepSMOTE: Fusing deep learning and SMOTE for imbalanced data D Dablain, B Krawczyk, NV Chawla IEEE Transactions on Neural Networks and Learning Systems 34 (9), 6390 - 6404, 2023 | 217 | 2023 |
Clustering-based ensembles for one-class classification B Krawczyk, M Woźniak, B Cyganek Information Sciences 264, 182-195, 2014 | 163 | 2014 |
Kappa Updated Ensemble for Drifting Data Stream Mining A Cano, B Krawczyk Machine Learning 109 (1), 175–218, 2020 | 155 | 2020 |
Towards highly accurate coral texture images classification using deep convolutional neural networks and data augmentation A Gómez-Ríos, S Tabik, J Luengo, ASM Shihavuddin, B Krawczyk, ... Expert Systems with Applications 118, 315-328, 2019 | 147 | 2019 |
Radial-based oversampling for noisy imbalanced data classification M Koziarski, B Krawczyk, M Woźniak Neurocomputing 343, 19-33, 2019 | 135 | 2019 |
An ensemble classification approach for melanoma diagnosis G Schaefer, B Krawczyk, ME Celebi, H Iyatomi Memetic Computing 6, 233-240, 2014 | 123 | 2014 |
Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data Z Zhang, B Krawczyk, S Garcia, A Rosales-Pérez, F Herrera Knowledge-Based Systems 106, 251-263, 2016 | 117 | 2016 |
Online ensemble learning with abstaining classifiers for drifting and noisy data streams B Krawczyk, A Cano Applied Soft Computing 68, 677-692, 2018 | 113 | 2018 |
Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise M Koziarski, M Woźniak, B Krawczyk Knowledge-Based Systems 204, 106223, 2020 | 106 | 2020 |
Synthetic oversampling with the majority class: A new perspective on handling extreme imbalance S Sharma, C Bellinger, B Krawczyk, O Zaiane, N Japkowicz 2018 IEEE international conference on data mining (ICDM), 447-456, 2018 | 106 | 2018 |
One-class classifiers with incremental learning and forgetting for data streams with concept drift B Krawczyk, M Woźniak Soft Computing 19 (12), 3387-3400, 2015 | 105 | 2015 |
Monotonic classification: An overview on algorithms, performance measures and data sets JR Cano, PA Gutiérrez, B Krawczyk, M Woźniak, S García Neurocomputing 341, 168-182, 2019 | 98 | 2019 |
Radial-Based Oversampling for Multiclass Imbalanced Data Classification B Krawczyk, M Koziarski, M Woźniak IEEE Transactions on Neural Networks and Learning Systems 31 (8), 2818-2831, 2020 | 96 | 2020 |