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prof. dr hab. inż.

Michał Woźniak


Kierownik katedry

Zespół Uczenia Maszynowego

Zainteresowania badawcze
Machine Learning
Pattern Recognition
Classifier Ensemble
Data Stream Mining

Prowadzi w tym semestrze
Seminarium dyplomowe

info Consultation hours: Wednesday 9a.m.-11p.m. (please email me earlier)

Fields of interest
  • 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,
  • imabalanced data classification,
  • pattern recognition with context
  • telemedicine and medical decision support

Google Scholar profile
ORCID
ResearchGate profile
ResearchID profile
List of publication in DONA database - maintained by my University

New publications
  1. Michał Koziarski, Colin Bellinger, Michał Woźniak, RB-CCR: Radial-Based Combined Cleaning and Resampling algorithm for imbalanced data classification, Machine Learning (ACCEPTED)
  2. Katarzyna Stąpor, Paweł Ksieniewicz, Salvador Garcia, Michał Woźniak, How to design the fair experimental classifier evaluation Applied Soft Computing, Volume 104, June 2021, 107219 FREE ACCESS BY April,27.
  3. Joanna Grzyb, Jakub Klikowski, Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification, Journal of Computational Science, Volume 51, April 2021, 101314 (free access by April 03, 2021) arxiv
  4. Michał Choraś, Konstantinos Demestichas, Agata Giełczyk, Álvaro Herrero, Paweł Ksieniewicz, Konstantina Remoundou, Daniel Urda, Michał Woźniak, Advanced Machine Learning techniques for fake news (online disinformation) detection: A systematic mapping study, Applied Soft Computing, Volume 101, March 2021, 107050. arxiv
  5. Paweł Zyblewski, Robert Sambourin, Michał Woźniak, Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams, Information Fusion, Volume 66, February 2021, Pages 138-154.
MSc thesis topics
  1. Fake news detection.
  2. Video/image manipulation discovery.
  3. Explainable AI/ML.
  4. Data stream classification.
  5. Learning from imabalanced data.

today
Rozkład zajęć
  • Środa
  • Seminarium dyplomowe C-4: 33 | 13.15—15.00
  • Seminarium dyplomowe C-4: 33 | 15.15—16.55

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