E-mail: michal.wozniak@pwr.edu.pl

Unit: Faculty of Information and Communication Technology (N) » Department of Systems and Computer Networks

ul. Z. Janiszewskiego 11/17, 50-372 Wrocław
building C-3, room 114
phone +48 71 320 3539

Office hours

• Monday 11.00-13.00
• Wednesday 11.00-13.00

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Research fields

• Machine learning; data stream mining; data stream classification, concept drift; ensemble learning, classifier ensemble; inductive learning, data and web mining; learning on distributed and streaming data; pattern classification; imabalance data classification; classification with recognition with context; telemedicine and medical decision support.

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Recent papers

2017

• Koziarski M., Woźniak M., CCR: A combined cleaning and resampling algorithm for imbalanced data classification, Int. J. Appl. Math. Comput. Sci., Vol. 27, No. 4, 727-736.
• Krawczyk B., Minku L., Gama J., Stefanowski J., Woźniak M., Ensemble learning for data stream analysis: A survey, Information Fusion, Vol. 37, pp. 132-156.
• Ksieniewicz P., Grana M., Woźniak M., Paired feature multilayer ensemble - concept and evaluation of a classifier, Journal of Inteligent and Fuzzy Systems, vol. 32, issue 2, pp. 1427-1436.
• Ramirez-Gallego S., Krawczyk B., Garcia S., Benitez M.J., Wozniak M., Herrera F., Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark, IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, vol. 46, issue 10, pp. 2727-2739.

2016

• Saez J.A., Krawczyk B., Woźniak M., Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets, Pattern Recognition, vol. 57, pp. 164-178.

Papers in DONA database

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Selected publications
1	Article 2024 Huanyu Guo, Xin Jin, Qin Jiang, Michał Woźniak, Puming Wang, Shaowen Yao, DMF-Net: a dual remote sensing image fusion network based on multi-scale convolutional dense connectivity with performance measure. IEEE Transactions on Instrumentation and Measurement. 2024, vol. 73, s. 1-15. ISSN: 0018-9456; 1557-9662 Resources:DOIURLSFX
2	Proceeding paper 2023 Jędrzej Z Kozal, Justyna Zwolińska, Marek Klonowski, Michał Woźniak, Defending network IDS against adversarial examples with continual learning. W: 23nd IEEE International Conference on Data Mining Workshops (ICDMW), 1–4 December 2023 Shanghai, China : proceedings / eds. Jihe Wang [i in.]. Piscataway, NJ : Institute of Electrical and Electronics Engineers, cop. 2023. s. 60-69. ISBN: 979-8-3503-8164-1; 979-8-3503-8165-8 Resources:DOIURL
3	Proceeding paper 2023 Jędrzej Z Kozal, Michał Woźniak, Combining self-labeling with selective sampling. W: 23nd IEEE International Conference on Data Mining Workshops (ICDMW), 1–4 December 2023 Shanghai, China : proceedings / eds. Jihe Wang [i in.]. Piscataway, NJ : Institute of Electrical and Electronics Engineers, cop. 2023. s. 70-79. ISBN: 979-8-3503-8164-1; 979-8-3503-8165-8 Resources:DOIURL
4	Proceeding paper 2023 Weronika T Węgier, Michał M Koziarski, Michał Woźniak, Optimized hybrid imbalanced data sampling for decision tree training. W: GECCO’23 Companion : Proceedings of the 2023 Genetic and Evolutionary Computation Conference Companion, July 15-19, 2023, Lisbon, Portugal / ed. Luís Paquete. New York, NY : ACM, cop. 2023. s. 339-342. ISBN: 979-8-4007-0120-7 Resources:DOIURL
5	Article 2023 Jędrzej Z Kozal, Michał Woźniak, Increasing depth of neural networks for life-long learning. Information Fusion. 2023, vol. 98, art. 101829, s. 1-10. ISSN: 1566-2535; 1872-6305 Resources:DOIURLSFX
6	Article 2023 Joanna A Klikowska, Michał Woźniak, SVM ensemble training for imbalanced data classification using multi-objective optimization techniques. Applied Intelligence. 2023, vol. 53, s. 15424-15441. ISSN: 0924-669X; 1573-7497 Resources:DOIURLSFX
7	Article 2023 Michał Woźniak, Paweł Zyblewski, Paweł Ksieniewicz, Active weighted aging ensemble for drifted data stream classification. Information Sciences. 2023, vol. 630, s. 286-304. ISSN: 0020-0255; 1872-6291 Resources:DOIURLSFX
8	Article 2023 Michał Panek, Adam Pomykała, Michał Woźniak, 5G/5G+ network management employing AI-based continuous deployment. Applied Soft Computing. 2023, vol. 134, art. 109984, s. 1-13. ISSN: 1568-4946; 1872-9681 Resources:DOIURLSFX
9	Proceeding paper 2022 Sebastián Basterrech, Michał Woźniak, Tracking changes using Kullback-Leibler divergence for the continual learning. W: 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) October 9-12, 2022, Prague, Czech Republic : proceedings. Danvers, MA : IEEE, cop. 2022. s. 3279-3285. ISBN: 978-1-6654-5259-5; 978-1-6654-5258-8 Resources:DOIURL
10	Article 2022 Jakub Klikowski, Michał Woźniak, Deterministic sampling classifier with weighted bagging for drifted imbalanced data stream classification. Applied Soft Computing. 2022, vol. 122, art. 108855, s. 1-18. ISSN: 1568-4946; 1872-9681 Resources:DOIURLSFX

All publications