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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
Uczenie maszyn

info Consultation hours: Mo 11a.m.-1p.m,

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. Paweł Zyblewski, Robert Sambourin, Michał Woźniak, Preprocessed dynamic classifier ensemble selection for highly imbalanced drifted data streams - free access till end of October, Information Fusion, Volume 66, February 2021, Pages 138-154.
  2. Bartosz Krawczyk, Michał Koziarski, Michał Woźniak, Radial-Based Oversampling for Multi-Class Imbalanced Data Classification, IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 8, pp. 2818-2831, Aug. 2020, doi: 10.1109/TNNLS.2019.2913673.
  3. Michał Koziarski, Michał Woźniak, Bartosz Krawczyk, Combined cleaning and resampling algorithm for multi-class imbalanced data with label noise, Knowledge-Based Systems, Volume 204, 27 September 2020, 106223.
  4. Amgad M. Mohammed, Enrique Onieva, Michał Wozniak, Training set selection and swarm intelligence for enhanced integration inmultiple classifier systems - - free access till September 15, Applied Soft Computing, Volume 95, October 2020, 106568.
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
  • Poniedziałek
  • Uczenie maszyn C-1: sala wirtualna | 9.15—11.00
  • Środa
  • Uczenie maszyn C-1: sala wirtualna | 9.15—11.00

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