Dariusz Brzezinski
Cytowane przez
Cytowane przez
Reacting to different types of concept drift: The accuracy updated ensemble algorithm
D Brzezinski, J Stefanowski
IEEE Transactions on Neural Networks and Learning Systems 25 (1), 81-94, 2013
Open challenges for data stream mining research
G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ...
ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014
Accuracy updated ensemble for data streams with concept drift
D Brzeziński, J Stefanowski
Hybrid Artificial Intelligent Systems, 155-163, 2011
Combining block-based and online methods in learning ensembles from concept drifting data streams
D Brzezinski, J Stefanowski
Information Sciences 265, 50-67, 2014
Prequential AUC: properties of the area under the ROC curve for data streams with concept drift
D Brzezinski, J Stefanowski
Knowledge and Information Systems 52 (2), 531-562, 2017
Mining data streams with concept drift
D Brzeziński
Cs Put Pozna 89, 2010
Prequential AUC for classifier evaluation and drift detection in evolving data streams
D Brzezinski, J Stefanowski
International Workshop on New Frontiers in Mining Complex Patterns, 87-101, 2014
Visual-based analysis of classification measures and their properties for class imbalanced problems
D Brzezinski, J Stefanowski, R Susmaga, I Szczȩch
Information Sciences 462, 242-261, 2018
Ligand‐centered assessment of SARS‐CoV‐2 drug target models in the Protein Data Bank
A Wlodawer, Z Dauter, IG Shabalin, M Gilski, D Brzezinski, M Kowiel, ...
The FEBS Journal 287 (17), 3703-3718, 2020
On the dynamics of classification measures for imbalanced and streaming data
D Brzezinski, J Stefanowski, R Susmaga, I Szczech
IEEE transactions on neural networks and learning systems 31 (8), 2868-2878, 2019
Ensemble diversity in evolving data streams
D Brzezinski, J Stefanowski
International conference on discovery science, 229-244, 2016
Clustering XML documents by patterns
M Piernik, D Brzezinski, T Morzy
Knowledge and Information Systems 46 (1), 185-212, 2016
XML clustering: a review of structural approaches
M Piernik, D Brzezinski, T Morzy, A Lesniewska
The Knowledge Engineering Review 30 (3), 297-323, 2015
Automatic recognition of ligands in electron density by machine learning
M Kowiel, D Brzezinski, PJ Porebski, IG Shabalin, M Jaskolski, W Minor
Bioinformatics 35 (3), 452-461, 2019
Stream Classification.
J Stefanowski, D Brzezinski
Encyclopedia of Machine Learning and Data Mining, 1191-1199, 2017
Conformation-dependent restraints for polynucleotides: I. Clustering of the geometry of the phosphodiester group
M Kowiel, D Brzezinski, M Jaskolski
Nucleic acids research 44 (17), 8479-8489, 2016
PUT at SemEval-2016 Task 4: The ABC of Twitter sentiment analysis
M Lango, D Brzezinski, J Stefanowski
Proceedings of the 10th International Workshop on Semantic Evaluation …, 2016
Covid‐19. bioreproducibility. org: A web resource for SARS‐CoV‐2‐related structural models
D Brzezinski, M Kowiel, DR Cooper, M Cymborowski, M Grabowski, ...
Protein Science 30 (1), 115-124, 2021
Accurate geometrical restraints for Watson–Crick base pairs
M Gilski, J Zhao, M Kowiel, D Brzezinski, DH Turner, M Jaskolski
Acta Crystallographica Section B: Structural Science, Crystal Engineering …, 2019
XCleaner: A new method for clustering XML documents by structure
D Brzeziński, A Leśniewska, T Morzy, M Piernik
Control and Cybernetics 40 (3), 877-891, 2011
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