Some of my papers (with links to their electronic versions);
For a complete list of my publications see Google Scholar profile
 

Monographs
My Habilitation Thesis
J.Stefanowski: Algorytmy indukcji regul decyzyjnych w odkrywaniu wiedzy, Rozprawa habilitacyjna; Rule induction algorithms for knowledge discovery (Published in Polish); Wydawnictwo Politechniki Poznanskiej, Seria Rozprawy, nr. 361, Poznan, 2001, 151 str. [English language translation of the title -- Algorithms of rule induction for knowledge discovery] Pdf free version of Polish author manuscript; Ziped Ps version of Polish manuscript; English Abstract.
 
 
Some of my papers [ordered from the newest ones]:

• B.Przybyl, J.Stefanowski: An appendix to the paper Improving Online Bagging for Complex Imbalanced Data Streams An pre-print appendix to the main paper (August 2024).
• Patryk Wielopolski, Oleksii Furman, Jerzy Stefanowski, Maciej Zięba: Probabilistically Plausible Counterfactual Explanations with Normalizing Flows. Preprint of the paper accepted for ECAI 2024 Conference.
• Ignacy Stępka, Mateusz Lango, Jerzy Stefanowski: A multi-criteria approach for selecting an explanation from the set of counterfactuals produced by an ensemble of explainers. Published in International Journal of Mathematics and Computer Science, (March 2024).
• D Brzezinski, J Stachowiak, J Stefanowski, I Szczech, R Susmaga, et al.: Properties of fairness measures in the context of varying class imbalance and protected group ratios. Published in ACM Transactions on Knowledge Discovery from Data (2024).
• Riccardo Albertoni, Sara Colantonio, Piotr Skrzypczynski, Jerzy Stefanowski: Reproducibility of Machine Learning: Terminology, Recommendations and Open Issues . A draft version of our recent paper available online at arXiv.2302.12691.
• Jakub Raczyński, Mateusz Lango, Jerzy Stefanowski: The Problem of Coherence in Natural Language Explanations of Recommendations. 26th European Conference on Artificial Intelligence ECAI 2023 (open access).
• J.Stefanowski: Przyrostowe uczenie sie klasyfikatorow ze zmiennych i niezbalansowanych strumieni danych (Incremental learning classifiers from concept drifting and imbalanced data streams) . Lecture slides [in Polish] - presented at the meeting of the Committee of Computer Sciences, Polish Academy of Science, Komitet Informatyki PAN, May 11, 2022.
• Lipska, Agnieszka, and Jerzy Stefanowski: The Influence of Multiple Classes on Learning from Imbalanced Data Streams. In Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, (organized jointly with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD 2022). Proceedings of Machine Learning Research, vol 183, 187-198, 2022. open access PMLR page version .
• Jerzy Stefanowski: Discussion of the possibilities of implementing selected guidelines for trustworthy artificial intelligence. 7th Int. Conference „Filozofia w Informatyce”, UAM Poznan, December 2-3 2022, Extended abstracts at the conference web page .
• Maciej Falbogowski, Jerzy Stefanowski, Zuzanna Trafas, Adam Wojciechowski: The Impact of Using Constraints on Counterfactual Explanation. Proceedings of the 3rd Polish Conference on Artificial Intelligence, April 25-27, 2022, Gdynia, Poland, 81-84, Gdynia Maritime University Press (2022) open access free version .
• Brzezinski, Dariusz, Minku, L.Leandro, Pewinski, Tomasz, Stefanowski Jerzy, Szymaczuk Artur: The impact of data difficulty factors on classification of imbalanced and concept drifting data streams. Knowledge Information Systems, 63, 1429-1469 (2021) open access free version .
• Grycza J., Horna D., Klimczak H., Lango M., Plucinski K, Stefanowski J., multi-imbalance: open source Python toolbox for multi-class imbalanced classification, European Conference on Machine Learning (ECML-PKDD), Ghent, Belgium,?2020 see open access conference presentation . For the conference paper see go to the bottom of this page.
• Nalepa J. Grzegorz, Stefanowski Jerzy: Artificial intelligence research community and associations in Poland.Foundations of Computing and Decision Sciences 45, no. 3 (2020). open access version .
• Malgorzata Janicka, Mateusz Lango, Jerzy Stefanowski: Using information on class interrelations to improve classification of multiclass imbalanced data: A new resampling algorithm. International Journal of Applied Mathematics and Computer Science, 29 (4), 769-781 (2019) open access version .
• Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczech. On the dynamics of classification measures for imbalanced and streaming data. IEEE transactions on neural networks and learning systems. online early access at the Publisher web page .
• Mateusz Lango, Jerzy Stefanowski: SOUP-Bagging: a new approach for multi-class imbalanced data classification. PP-RAI 2019 Conference Proceedings, 292 - 295. Predraft authors' version
• Lango, M., Brzezinski, D. and Stefanowski, J.: ImWeights: Classifying Imbalance Data Using Local and Neighborhood Information. 2nd International Workshop on Learning with Imbalanced Domains: Theory and Applications, LIDTA 2018 jointly with ECMLPKDD 2018 Conference, Dublin, Ireland, September 10--14, 2018, JMLR Workshop Proceedings (to appear) Prefdraft author version also available at the workshop page.
• Dariusz Brzezinski, Jerzy Stefanowski, Robert Susmaga, Izabela Szczech: Visual-based analysis of classification measures and their properties for class imbalanced problems. Inf. Sci. 462: 242-261 (2018) from the journal page see also an early predraft version at arxiv,org.
• Ireneusz Czarnowski, Krzysztof Krawiec, Jacek Mandziuk, Jerzy Stefanowski: Raport z pierwszego Zjazdu Polskiego Porozumienia na Rzecz Rozwoju Sztucznej Inteligencji PP-RAI 2018. free access manuscript
• Mateusz Lango, Jerzy Stefanowski: Multi-class and feature selection extensions of Roughly Balanced Bagging for imbalanced data. Journal of Intelligent Information Systems, 50(1): 97-127 (2018); free open access at Springer JIIS journal page .
• Jerzy Stefanowski, Krzysztof Krawiec, Robert Wrembel: Exploring complex and big data. Applied Mathematics and Computer Science 27(4): 669-679 (2017) free open access at Sciendo journal page .
• Dariusz Brzezinski, Jerzy Stefanowski: Prequential AUC: Properties of the Area Under the ROC Curve for Data Streams with Concept Drift. Knowledge and Information Systems 2017 (accepted); free open access at Springer KAIS journal page .
• Jerzy Blaszczynski, Jerzy Stefanowski: Local data characteristics in learning classifiers from imbalanced data. A chapter in: Advances in Data Analysis with Computational Intelligence Methods , Springer Verlag, 2018. 51-85. See a the last accepted author version.
• Jerzy Blaszczynski, Jerzy Stefanowski: Actively Balanced Bagging for Imbalanced Data. Proc. of the 23th ISMIS 2017, Springer Verlag 271-281. See a preprint author version.
• Mateusz Lango, Krystyna Napierala, Jerzy Stefanowski: Evaluating Difficulty of Multi-class Imbalanced Data. Proc. of the 23th ISMIS 2017, Springer Verlag, 312-322. See a preprint author version.
• Krawczyk Bartosz, Minku Leandro L, Gama Joao, Stefanowski Jerzy, Wozniak, Michal: Ensemble learning for data stream analysis: A survey. Information Fusion, vol 37, 2017 (in press); standard access at Elsevier journal page + the last post-review accepted author version .
• Krystyna Napierala, Jerzy Stefanowski, Izabela Szczech: Increasing the Interpretability of Rules Induced from Imbalanced Data by Using Bayesian Confirmation Measures . In Annalisa Appice et al (Eds) Proceedings of the 5th Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2016) - ECML PKDD 2016, Riva del Garda. See an electronic proceedings version at the workshop web page . An extended version will appear in Springer LNCS Post-proceeding 2017.
• Dariusz Brzezinski, Jerzy Stefanowski: Ensemble Diversity in Evolving Data Streams. In Discovery Science. 19th International Conference, DS 2016, Bari, Italy, October 19-21, (accepted); full proceedings will be in Springer LNAI. See preprint author version.
• Dariusz Brzezinski, Jerzy Stefanowski: Stream Classification. In Encyclopedia of Machine Learning, Springer 2016 (accepted). See preprint author version.
• Jerzy Stefanowski: Dealing with Data Difficulty Factors while Learning from Imbalanced Data. Full version in S. Matwin and J. Mielniczuk (eds.), Challenges in Computational Statistics and Data Mining, Studies in Computational Intelligence 605, DOI 10.1007/978-3-319-18781-5_17 (2016) pp. 333--363 . See also the accepted author version.
• Mateusz Lango, Jerzy Stefanowski: Applicability of Roughly Balanced Bagging for Complex Imbalanced Data. In Michelangelo Ceci et al (Eds) Proceedings of the 4th Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2015) - ECML PKDD 2015, Porto, September 7, pages 62-73. See an electronic proceedings version at the workshop web page, or a accepted author version.
• Krystyna Napierala, Jerzy Stefanowski: Types of minority class examples and their influence on learning classifiers from imbalanced data. Journal of Intelligent Information Systems, 563--597 (2016). see - on-line first, open access.
• Jerzy Błaszczynski, Jerzy Stefanowski: Neighbourhood sampling in bagging for imbalanced data. Full version in Neurocomputing, vol 150, 529-542 (2015). See a preprint author version.  
• Dariusz Brzezinski, Jerzy Stefanowski: Prequential AUC for Classifier Evaluation and Drift Detection in Evolving Data Streams. In Proceeding of the ECML/PKDD 2014 Workshop on New Frontiers in Mining Complex Patterns, See workshop proceedings -- An extended version in Post-Proceedings of New Frontiers in Mining Complex Patterns, LNCS Vol. 8983, pp. 87-101 - see local pre-print .
• Magdalena Deckert, Jerzy Stefanowski: RILL: Algorithm for Learning Rules from Streaming Data with Concept Drift. In Foundations of Inteligent Systems. Proc. 21th Int. Symposium ISMIS 2014, 20-29. See a preprint author version.
• Dariusz Brzezinski, Jerzy Stefanowski: Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm. IEEE Transactions on Neural Networks and Learning Systems Volume 25, Issue 1, January 2014, 81 - 94. See a preprint author version
• Dariusz Brzezinski, Jerzy Stefanowski: Classifiers for Concept-drifting Data Streams: Evaluating Things That Really Matter. In Proceeding of the ECML/PKDD 2013 Workshop on Real-World Challenges for Data Stream Mining, ECML PKDD 2013 September 27th, Prague, Czech Republic, 2013, 10-13.. See workshop proceedings or a local pdf repository
• Jerzy Blaszczynski, Jerzy Stefanowski, Lukasz Idkowiak: Extending bagging for imbalanced data. In Proc. 8th CORES, Springer Series on Advances in Intelligent Systems and Computing 226, 2013, 269–278; See a preprint author version.
• Jerzy Stefanowski: Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Data. Chapter in L. Jain, R.Howlett, S.Ramanna (Eds). Emerging Paradigms in Machine Learning and Applications, Springer Verlag Smart Innovation, Systems and Technologies Volume 13, 2013, 277-306 - see Springer Link to electronic resources or a preprint author version/
• Krystyna Napierala, Jerzy Stefanowski: BRACID: a comprehensive approach to learning rules from imbalanced data. Journal of Intelligent Information Systems, Volume 39, Number 2, 2012, 335-373. DOI: 10.1007/s10844-011-0193-0 - see Springer Link to electronic resources / -- online first option with an open access.
• Dariusz Brzezinski, Jerzy Stefanowski: From Batch Ensembles to Incremental Learners: If- and How-To. In Proceeding of the ECML/PKDD 2012 Workshop on Instant Interactive Data Mining, September 24th, Bristol, UK pages. See workshop proceedings or a local pdf repository/
• K.Napierala, J.Stefanowski: Modification of Classification Strategies in Rule Set Based Bagging for Imbalanced Data. In: E. Corchado, V. Snasel, A.Abraham, M. Wozniak et al. (eds): Hybrid Artificial Intelligence Systems, Proc. 7th Int Conference HAIS 2012, Part II, Lecture Notes in Artificial Intelligence vol. 7209, Springer Verlag 2012, 514-525. - see Springer Link to electronic resources
• K.Napierala, J.Stefanowski: Identification of Different Types of Minority Class Examples in Imbalanced Data. In: E. Corchado, V. Snasel, A.Abraham, M. Wozniak et al. (eds): Hybrid Artificial Intelligence Systems, Proc. 7th Int Conference HAIS 2012, Part II, Lecture Notes in Artificial Intelligence vol. 7209, Springer Verlag 2012, 139-150. - see Springer Link to electronic resources
• Dariusz Brzezinski, Jerzy Stefanowski: Accuracy Updated Ensemble for Data Streams with Concept Drift. In Proc. HAIS 2011, Springer Verlag Lecture Notes in Artificial Intelligence 6679, 2011,155-163. - see Springer Link to electronic resources; -- or its draft version
• Tomasz Maciejewski, Jerzy Stefanowski: Local Neighbourhood Extension of SMOTE for Mining Imbalanced Data. In Proc. IEEE Symposium on Computational Intelligence and Data Mining CIDM 2011. Within 2011 IEEE SSCI, Paris, 11-15 April 2011, IEEE Press, 104-111.
• Krystyna Napierala, Jerzy Stefanowski, Szymon Wilk: Learning from imbalanced data in presence of noisy and borderline examples RSCTC 2010, Springer Verlag Lecture Notes in Artificial Intelligence 6086, 2010, 148-157. - see Springer Link to electronic resources; -- -- Its draft version is here
• Jerzy Blaszczynski, Magdalena Deckert, Jerzy Stefanowski, Szymon Wilk: Integrating Selective Pre-processing of Imbalanced Data with Ivotes Ensemble RSCTC 2010, Springer Verlag Lecture Notes in Artificial Intelligence 6086, 2010, 158-167. - see Springer Link to electronic resources. -- Its draft version is here
• Krystyna Napierala, Jerzy Stefanowski: Argument Based Generalization of MODLEM Rule Induction Algorithm. RSCTC 2010, Springer Verlag Lecture Notes in Artificial Intelligence 6086, 2010, 138-147 - see Springer Link to electronic resources.
• Milosz Kmieciak, Jerzy Stefanowski: Handling Sudden Concept Drift in Enron Messages Data Stream. draft version of a paper finally published in T. Morzy, M.Gorawski, R.Wrembel, A.Zgrzywa (ed.) Technologie przetwarzania danych. Mat. III KNTPD Conf., Poznan 21-23 April 2010, WNT Press, 2010, 284-296
• J.Blaszczynski, R.Słowinski, J.Stefanowski: Feature Set-based Consistency Sampling in Bagging Ensembles. Proceedings of the Workshop From Local to Global Models at the European Conference on Machine Learning and Principles of Knowledge Discovery in Databases, 7-11 Sept. 2009, Bled, pp. 19-35 / A pdf file available from ECML/PKDD WWW .
• J.Stefanowski, M.Pachocki: Comparing Performance of Committee based Approaches to Active Learning. In: Proceedings of XVth Intelligent Information Systems IIS’2008, pp. 457-470.
• J.Blaszczynski, J.Stefanowski, M.Zajac: Ensembles of Abstaining Classifiers based on Rule Sets. In: J.Rauch, Z.Ras, P.Berka, T.Eloma (eds.): Foundations of Inteligent Systems. Proc. 18 Int. Symposium ISMIS 2009, Springer Verlag Lecture Notes in Artificial Intelligence 5722, 2008, 382-391 - see Springer Link to electronic resources. -- Its draft version is here
• J.Stefanowski, Sz.Wilk: Extending Rule-Based Classifiers to Improve Recognition of Imbalanced Classes. In: Zbigniew W. Ras, Agnieszka Dardzińska (eds.): Advances in Data Management, Studies in Computational Intelligence vol. 223, 2009, 131-154. - see Springer Link.
• J.Stefanowski, Sz.Wilk: Selective Pre-processing of Imbalanced Data for Improving Classification Performance. In: I.Y.Song, J.Eder, T.Nguyen (eds.) Proc. of 10th Int. Conference DaWaK 2008, Lecture Notes in Computer Science vol. 5182, Springer Verlag, 2008, 283-292; - see Springer Link. -- Its draft version is here
• J.Stefanowski, Sz.Wilk: Improving Rule Based Classifiers Induced by MODLEM by Selective Pre-processing of Imbalanced Data. In: Proceedings of the Workshop "RSKD" at European Conference on Machine Learning and Principles of Knowledge Discovery in Databases, 17-21 September 2007 Warszawa, 54-65.
• J. Stefanowski: On Combined Classifiers, Rule Induction and Rough Sets. In: J.F. Peters et al. (Eds.): Transactions on Rough Sets VI, LNCS 4374, pp. 329–350, 2007. Springer-Verlag Berlin Heidelberg 2007 - the author last accepted version.
• S.Greco, R.Slowinski, J.Stefanowski: Evaluating Importance of Conditions in the Set of Discovered Rules. In: Aijun An et al (eds.): Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, 11th International Conference, RSFDGrC 2007, Toronto, Canada, May 14-16, 2007, Proceedings. Lecture Notes in Computer Science, vol. 4482, Springer Verlag 2007, 314-321; - see Springer Link.
• J.Stefanowski, M.Zienkowicz: Classification of Polish Email Messages: Experiments with Various Data Representations. In: F.Esposito et al. (eds.) Foundations of Intelligent Systems. 16 Int. Symposium ISMIS 2006, Springer Verlag Lecture Notes in Artificial Intelligence 4203, 2006, 723-728; - see Springer Link.
• J.Stefanowski, S.Wilk: Combining Rough Sets and Rule based Classifiers for Handling Imbalanced Data. In: Czaja L. (ed.) Proceedings of Concurrency, Specification and Programming CS&P 2005 Conference, vol. 2, 2005, 497-508.
• J.Stefanowski: Extending Rule Based Classifiers for Dealing with Imbalanced Data. In: T.Burczynski, W.Cholewa, W.Moczulski (ed.) Proceedings of the AI-METH 2005: Recent Developments in Artificial Intelligence Methods, AI-Meth Series, Gliwice 2005, 203–208.
• J.Stefanowski: An Experimental Study of Methods Combining Multiple Classifiers - Diversified both by Feature Selection and Bootstrap Sampling. In Krassimir T. Atanassov, Janusz Kacprzyk, Maciej Krawczak, Eulalia Szmidt (eds) Issues in the Representation and Processing of Uncertain and Imprecise Information. Akademicka Oficyna Wydawnicza EXIT, Warszawa, 2005, 337-354.
• S. Osiński, J. Stefanowski, D. Weiss: Lingo: Search Results Clustering Algorithm Based on Singular Value Decomposition. In: Advances in Soft Computing, Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM´04 Conference, 2004, 359—368.
• J.Stefanowski, M. Zurawski: Incremental Rule Induction for Multicriteria and Multiattribute Classification. In: M.A.Kłopotek, S.T.Wierzchoń, K.Trojanowski (eds.), Intelligent Information Processing and Web Mining, Proceedings of the International IIS: IIPWM´03 Conference, 2003, 311-320.
• J.Stefanowski, D. Weiss: Carrot2 and Language Properties in Web Search Results Clustering. In: Menasalvas E., Segovia J. Szczepaniak P. (eds.) Advances in Web Intelligence, Proceedings of the First International Atlantic Web Intelligence Conference, Madrid, Spain, May 2003. A shorter version to be published as Springer LNCS.
• J.Stefanowski: Changing representation of learning examples while inducing classifiers based on decision rules., [in]: Burczyński T., Cholewa W., Moczulski (eds): Proceedings of the AI-METH 2003 - Symposium on Methods of Artificial Intelligence, Gliwice, Poland November 5-7, 2003, pp. 297-301.
• J.Jelonek, J.Stefanowski: Feature selection in the n2-classifier applied for multiclass problems, [in]: Burczyński T., Cholewa W., Moczulski (eds): Proceedings of the AI-METH 2002 - Symposium on Methods of Artificial Intelligence, Gliwice, Poland November 13-15, 2002, pp. 197-200.
• J. Stefanowski, A.Tsoukias: Induction of Decision Rules and Classification in the Valued Tolerance. [In]: Proceedings of Rough Sets and Current Trends in Computing, RSCTC 2002, Malvern, 2002.
• S.Greco, R.Slowinski, J.Stefanowski: Mining association rules in preference-ordered data. The paper presented at the 13th Int. Symposium ISMIS 2002, Lyon France, June 2002; Published in Springer Verlag LNAI no. 2366, 2002, 442-451.
• S.Greco, B.Matarazzo, R.Slowinski, J.Stefanowski: An algorithm for induction of decision rules consistent with dominance principle. In: W.Ziarko, Y.Y.Yao (eds), Proc. 2nd Int. Conference on Rough Sets and Current Trends in Computing, Banff, October 16- 19, 2000, 266-275.
• J.Stefanowski: Multiple and Hybrid Classifiers , In: L. Polkowski (ed.), Formal Methods and Intelligent Techniques in Control, Decision Making, Multimedia and Robotics - Post- Proceedings of 2nd International Conference, (PJIIT, Warszawa October 2000), Warszawa, 2001, 174-188.
• J.Jelonek, J.Stefanowski: Experiments on Solving Multiclass Learning Problems by n2 classifier, [in]: Proceedings 10th European Conference on Machine Learning, Chemnitz April 21-24, 1998, Springer-Verlag Lecture Notes in Artificial Intelligence no. 1398, 1998, 172-177.
• J.Stefanowski, On rough set based approaches to induction of decision rules. Preliminary version of the paper published in A. Skowron, L. Polkowski (red.), Rough Sets in Knowledge Discovery Vol 1, Physica Verlag, Heidelberg, 1998, 500-529.
• J.Jelonek, K.Krawiec, J.Stefanowski, Comparative study of feature subset selection techniques for machine learning tasks, In: Proceedings of VIIth Intelligent Information Systems IIS’98, Malbork 15-19 June 1998, Wyd. Instytutu Podstaw Informatyki PAN, Warszawa, 68-77