Paweł Teisseyre, PhD

Publications

  1. Anna Skowrońska, Siamala Sinnadurai, Paweł Teisseyre, Patrycja Gryka, Agnieszka Doryńska, Magdalena Dzierwa, Mariusz Gąsior, Marcin Grabowski, Karol Kamiński, Jarosław D Kasprzak, Jacek Kubica, Maciej Lesiak, Bartosz Szafran, Mariusz Wójcik, Jarosław Pinkas, Radosław Sierpiński, Ryszard Gellert, Piotr Jankowski, First-year follow-up costs of myocardial infarction management in Poland from payer's perspective, Polish Heart Journal (Kardiologia Polska), 2024.
  2. Wangduk Seo, Sung-Hyun Cho, Paweł Teisseyre, Jaesung Lee, A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features, IEEE Access, 2024.
  3. Tomasz Klonecki, Paweł Teisseyre, Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods, Applied Intelligence, 2023.
  4. Małgorzata Kupisz-Urbańska, Piotr Jankowski, Roman Topór-Mądry, Michał Chudzik, Mariusz Gąsior, Robert Gil, Patrycja Gryka, Zbigniew Kalarus, Jacek Kubica, Jacek Legutko, Przemysław Mitkowski, Jarosław Pinkas, Radosław Sierpiński, Janina Stępińska, Zbigniew Siudak, Paweł Teisseyre, Adam Witkowski, Urszula Zielińska-Borkowska, Tomasz Zdrojewski, Ryszard Gellert, Survival in nonagenarians with acute myocardial infarction in 2014–2020: A nationwide analysis, Polish Heart Journal (Kardiologia Polska), 2023.
  5. Konrad Furmanczyk, Jan Mielniczuk, Wojciech Rejchel, Paweł Teisseyre, Double Logistic Regression Approach to Biased Positive-Unlabeled Data, Proceedings of the European Conference on Artificial Intelligence ECAI’23, 2023.
  6. Tomasz Klonecki, Paweł Teisseyre, Jaesung Lee, Cost-constrained Group Feature Selection Using Information Theory, Proceedings of the International Conference Modeling Decisions for Artificial Intelligence, MDAI'23, 2023.
  7. Tomasz Klonecki, Paweł Teisseyre, Jaesung Lee, Cost-constrained feature selection in multilabel classification using an information-theoretic approach, Pattern Recognition, 2023.
  8. Tae-Won Lee, Paweł Teisseyre, Jaesung Lee, Effective Exploitation of Macroeconomic Indicators for Stock Direction Classification using the Multimodal Fusion Transformer, IEEE Access, 2023.
  9. Paweł Teisseyre, Jaesung Lee, Multilabel all-relevant feature selection using lower bounds of conditional mutual information, Expert Systems with Applications, 2023.
  10. Paweł Teisseyre, Joint feature selection and classification for positive unlabelled multi-label data using weighted penalized empirical risk minimization, International Journal of Applied Mathematics and Computer Science, 2022.
  11. Ewelina Pośpiech, Paweł Teisseyre, Jan Mielniczuk, Wojciech Branicki, Predicting Physical Appearance from DNA Data—Towards Genomic Solutions, Genes, 2022.
  12. M. Kukla-Bartoszek, P. Teisseyre, E. Pośpiech, J. Karłowska-Pik, P. Zieliński, A. Woźniak, M. Boroń, M. Dąbrowski, M. Zubańska, A. Jarosz, R. Płoski, T. Grzybowski, M. Spólnicka, J. Mielniczuk, W. Branicki, Searching for improvements in predicting human eye colour from DNA, International Journal of Legal Medicine, 2021.
  13. P. Teisseyre, T. Klonecki, Controlling Costs in Feature Selection: Information Theoretic Approach, Proceedings of the International Conference on Computational Science ICCS’21, 2021.
  14. J. Mielniczuk, P. Teisseyre, Detection of Conditional Dependence Between Multiple Variables Using Multiinformation, Proceedings of the International Conference on Computational Science ICCS’21, 2021.
  15. 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.
  16. 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.
  17. P. Teisseyre, Classifier chains for positive unlabelled multi-label learning, Knowledge-Based Systems, Volume 213, 2021.
  18. P. Teisseyre, Learning classifier chains using matrix regularization: application to multi-morbidity prediction, Proceedings of the European Conference on Artificial Intelligence ECAI’20, 2020.
  19. 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.
  20. 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.
  21. 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.
  22. J. Mielniczuk, P. Teisseyre, Stopping rules for mutual information-based feature selection, Neurocomputing, Volume 358(17), 255-274, 2019.
  23. 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.
  24. M. Pawluk, P. Teisseyre, J. Mielniczuk, Information-Theoretic Feature Selection Using High-Order Interactions, Proceedings of the 4th International Conference LOD, Volterra, Italy, 2019.
  25. 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.
  26. 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.
  27. P. Teisseyre, CCnet: joint multi-label classification and feature selection using classifier chains and elastic net regularization, Neurocomputing, Volume 235, 98-111, 2017.
  28. 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.
  29. P. Teisseyre, Feature ranking for multi-label classification using Markov Networks, Neurocomputing, Volume 205, 439-454, 2016.
  30. 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 .
  31. 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.
  32. 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.
  33. P. Przybyła, P. Teisseyre, Analysing Utterances in Polish Parliament to Predict Speaker's Background, Journal of Quantitative Linguistics, Volume 21 (4), 2014.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. 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.

PhD thesis

  1. P. Teisseyre, On some methods of model selection for linear and logistic regression, PhD dissertation, Warsaw, 2013.