Bartosz Krawczyk
Bartosz Krawczyk
Assistant Professor, Center for Imaging Science, Rochester Institute of Technology, USA
Verified email at - Homepage
Cited by
Cited by
Learning from imbalanced data: open challenges and future directions
B Krawczyk
Progress in artificial intelligence 5 (4), 221-232, 2016
Learning from imbalanced data sets
A Fernández, S García, M Galar, RC Prati, B Krawczyk, F Herrera
Springer 10 (2018), 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
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
Cost-sensitive decision tree ensembles for effective imbalanced classification
B Krawczyk, M Woźniak, G Schaefer
Applied Soft Computing 14, 554-562, 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
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
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
Clustering-based ensembles for one-class classification
B Krawczyk, M Woźniak, B Cyganek
Information Sciences 264, 182-195, 2014
Kappa Updated Ensemble for Drifting Data Stream Mining
A Cano, B Krawczyk
Machine Learning 109 (1), 175–218, 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
Radial-based oversampling for noisy imbalanced data classification
M Koziarski, B Krawczyk, M Woźniak
Neurocomputing 343, 19-33, 2019
An ensemble classification approach for melanoma diagnosis
G Schaefer, B Krawczyk, ME Celebi, H Iyatomi
Memetic Computing 6, 233-240, 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
Online ensemble learning with abstaining classifiers for drifting and noisy data streams
B Krawczyk, A Cano
Applied Soft Computing 68, 677-692, 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
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
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
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
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
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