Paweł Teisseyre, PhD


  1. P. Teisseyre, T. Klonecki, Controlling Costs in Feature Selection: Information Theoretic Approach, Proceedings of the International Conference on Computational Science ICCS’21, 2021.
  2. J. Mielniczuk, P. Teisseyre, Detection of Conditional Dependence Between Multiple Variables Using Multiinformation, Proceedings of the International Conference on Computational Science ICCS’21, 2021.
  3. M. Łazęcka, J. Mielniczuk, P. Teisseyre, Estimating the class prior for positive and unlabelled data via logistic regression, Advances in Data Analysis and Classification, 2021.
  4. M. Kubkowski, J. Mielniczuk, P. Teisseyre, How to Gain on Power: Novel Conditional Independence Tests Based on Short Expansion of Conditional Mutual Information, Journal of Machine Learning Research, Volume 22(62), 1−57, 2021.
  5. P. Teisseyre, Classifier chains for positive unlabelled multi-label learning, Knowledge-Based Systems, Volume 213, 2021.
  6. P. Teisseyre, Learning classifier chains using matrix regularization: application to multi-morbidity prediction, Proceedings of the European Conference on Artificial Intelligence ECAI’20, 2020 (rank A conference).
  7. P. Teisseyre, Jan Mielniczuk, M. Łazęcka, Different strategies of fitting logistic regression for positive and unlabelled data, Proceedings of the International Conference on Computational Science ICCS’20, 2020 (rank A conference).
  8. P. Teisseyre, Jan Mielniczuk, M. J. Dąbrowski, Testing the Significance of Interactions in Genetic Studies Using Interaction Information and Resampling Technique, Proceedings of the International Conference on Computational Science ICCS’20, 2020 (rank A conference).
  9. M. Kukla-Bartoszek, E. Pospiech, A. Wozniak, M. Boron, J. Karlowska-Pik, P. Teisseyre, M. Zubanska, A. Bronikowska, T. Grzybowski, R. Ploski, M. Spolnicka, W. Branicki, DNA-based predictive models for the presence of freckles, Forensic Science International: Genetics, Volume 42, 252-259, 2019.
  10. J. Mielniczuk, P. Teisseyre, Stopping rules for mutual information-based feature selection, Neurocomputing, Volume 358(17), 255-274, 2019.
  11. P. Teisseyre, D. Zufferey, M. Slomka, Cost-sensitive classifier chains: Selecting low-cost features in multi-label classification, Pattern Recognition, Volume 86, 290-319, 2019.
  12. M. Pawluk, P. Teisseyre, J. Mielniczuk, Information-Theoretic Feature Selection Using High-Order Interactions, Proceedings of the 4th International Conference LOD, Volterra, Italy, 2019.
  13. M. J. Dabrowski, M. Draminski, K. Diamanti, K. Stepniak, M. A. Mozolewska, P. Teisseyre, J. Koronacki, J. Komorowski, B. Kaminska and B. Wojtas, Unveiling new interdependencies between significant DNA methylation sites, gene expression profiles and glioma patients survival, Scientific Reports, Volume 8, 1-12, 2018.
  14. J. Mielniczuk, P. Teisseyre, Deeper Look at Two Concepts of Measuring Gene-Gene Interactions: Logistic Regression and Interaction Information Revisited, Genetic Epidemiology, Volume 42 (2) 187-200, 2018.
  15. P. Teisseyre, CCnet: joint multi-label classification and feature selection using classifier chains and elastic net regularization, Neurocomputing, Volume 235, 98-111, 2017.
  16. M. Sydow, K. Baraniak, P. Teisseyre, Diversity of editors and teams versus quality of cooperative work: experiments on wikipedia, Journal of Intelligent Information Systems, Volume 48 (3), 601–632, 2017.
  17. P. Teisseyre, Feature ranking for multi-label classification using Markov Networks, Neurocomputing, Volume 205, 439-454, 2016.
  18. P. Teisseyre, R. A. Klopotek, J. Mielniczuk, Random Subspace Method for High-Dimensional Regression with the R Package regRSM, Computational Statistics, Volume 31(3), 943-972, 2016 .
  19. J. Mielniczuk, P. Teisseyre, What do we choose when we err? Model selection and testing for misspecied logistic regression revisited, Challenges in Computational Statistics and Data Mining, Volume: 605 of the series Studies in Computational Intelligence, 271--296, 2015.
  20. P. Przybyła, P. Teisseyre, What do your look-alikes say about you? Exploiting strong and weak similarities for author profiling, Notebook for PAN at CLEF 2015.
  21. P. Przybyła, P. Teisseyre, Analysing Utterances in Polish Parliament to Predict Speaker's Background, Journal of Quantitative Linguistics, Volume 21 (4), 2014.
  22. J. Mielniczuk, P. Teisseyre, Using Random Subspace Method for Prediction and Variable Importance Assesment in Regression, Computational Statistics and Data Analysis, Volume 71, 725-742, 2014.
  23. P. Teisseyre, On some methods of model selection for linear and logistic regression, PhD dissertation, Warsaw, 2013.
  24. J. Mielniczuk, P. Teisseyre, Selection of regression and autoregression models with initial ordering of variables, Communications in Statistics, Theory and Methods, Volume 41 (24), 4484 - 4502, 2012.
  25. J. Mielniczuk, P. Teisseyre, Selection and prediction for linear models using Random Subspace Methods, Proceedings of the Conference Information Technologies: Research and their Interdisciplinary Applications, 2012.
  26. J. Mielniczuk, P. Teisseyre, Model selection in logistic regression using p-values and greedy search, Security and Intelligent Information Systems. LNCS 7053, Springer-Verlag Berlin Heidelberg, 128-141, 2011.
  27. L. Stapp, M. Pilarski, P. Stapp, P. Zgadzaj, P. Teisseyre, Dynamic Time Warping as a method for observing load possibility for CDN clusters, Proceedings of The Second International Multi-Conference on Complexity, Informatics, Cybernetics, Orlando, Florida, USA, 2011.