Dariusz Brzezinski

Appl. Soft Comput., 2024

ART: Actually Robust Training.

[BibT_eX]

[DOI]

Sebastian Chwilczynski

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track and Demo Track, 2024

2023

MSD-space: Visualizing the inner-workings of TOPSIS aggregations.

[BibT_eX]

[DOI]

Eur. J. Oper. Res., July, 2023

The CausalBench challenge: A machine learning contest for gene network inference from single-cell perturbation data.

[BibT_eX]

[DOI]

Mathieu Chevalley

Jacob Sackett-Sanders

CoRR, 2023

confidence-planner: Easy-to-Use Prediction Confidence Estimation and Sample Size Planning.

[BibT_eX]

[DOI]

CoRR, 2023

2022

DBFE: distribution-based feature extraction from structural variants in whole-genome data.

[BibT_eX]

[DOI]

Weronika Majer-Burman

Bioinform., 2022

Random Similarity Forests.

[BibT_eX]

[DOI]

Pawel Zawadzki

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

2021

Recognizing and validating ligands with CheckMyBlob.

[BibT_eX]

[DOI]

Przemyslaw J. Porebski

Marcin Kowiel

Joanna M. Macnar

Wladek Minor

Nucleic Acids Res., 2021

The impact of data difficulty factors on classification of imbalanced and concept drifting data streams.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2021

Detecting anomalies in X-ray diffraction images using convolutional neural networks.

[BibT_eX]

[DOI]

Adam Czyzewski

Faustyna Krawiec

Przemyslaw J. Porebski

Wladek Minor

Expert Syst. Appl., 2021

2020

On the Dynamics of Classification Measures for Imbalanced and Streaming Data.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2020

Fibonacci and k-Subsecting Recursive Feature Elimination.

[BibT_eX]

[DOI]

CoRR, 2020

2019

Automatic recognition of ligands in electron density by machine learning.

[BibT_eX]

[DOI]

Marcin Kowiel

Przemyslaw J. Porebski

Ivan G. Shabalin

Mariusz Jaskolski

Wladek Minor

Bioinform., 2019

2018

Visual-based analysis of classification measures and their properties for class imbalanced problems.

[BibT_eX]

[DOI]

Inf. Sci., 2018

ImWeights: Classifying Imbalanced Data Using Local and Neighborhood Information.

[BibT_eX]

[DOI]

Mateusz Lango

Proceedings of the Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, 2018

2017

Stream Classification.

[BibT_eX]

[DOI]

Proceedings of the Encyclopedia of Machine Learning and Data Mining, 2017

Prequential AUC: properties of the area under the ROC curve for data streams with concept drift.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

Visual-Based Analysis of Classification Measures with Applications to Imbalanced Data.

[BibT_eX]

[DOI]

CoRR, 2017

Tetrahedron: Barycentric Measure Visualizer.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Using Network Analysis to Improve Nearest Neighbor Classification of Non-network Data.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 23rd International Symposium, 2017

Discovering Minority Sub-clusters and Local Difficulty Factors from Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science - 20th International Conference, 2017

2016

Clustering XML documents by patterns.

[BibT_eX]

[DOI]

Tadeusz Morzy

Knowl. Inf. Syst., 2016

PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis.

[BibT_eX]

[DOI]

Mateusz Lango

Proceedings of the 10th International Workshop on Semantic Evaluation, 2016

Bayesian Confirmation Measures in Rule-Based Classification.

[BibT_eX]

[DOI]

Zbigniew Grudzinski

Izabela Szczech

Proceedings of the New Frontiers in Mining Complex Patterns - 5th International Workshop, 2016

Ensemble Diversity in Evolving Data Streams.

[BibT_eX]

[DOI]

Proceedings of the Discovery Science - 19th International Conference, 2016

2015

Structural XML Classification in Concept Drifting Data Streams.

[BibT_eX]

[DOI]

New Gener. Comput., 2015

XML clustering: a review of structural approaches.

[BibT_eX]

[DOI]

Knowl. Eng. Rev., 2015

2014

Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2014

Open challenges for data stream mining research.

[BibT_eX]

[DOI]

SIGKDD Explor., 2014

Combining block-based and online methods in learning ensembles from concept drifting data streams.

[BibT_eX]

[DOI]

Inf. Sci., 2014

Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams.

[BibT_eX]

[DOI]

Proceedings of the New Frontiers in Mining Complex Patterns - Third International Workshop, 2014

Adaptive XML Stream Classification Using Partial Tree-Edit Distance.

[BibT_eX]

[DOI]

Proceedings of the Foundations of Intelligent Systems - 21st International Symposium, 2014

2011

Accuracy Updated Ensemble for Data Streams with Concept Drift.

[BibT_eX]

[DOI]