Kliegr, Tomáš, Ebroul Izquierdo. "QCBA: improving rule classifiers learned from quantitative data by recovering information lost by discretisation" Applied Intelligence. Springer. 2023. Also in CRAN package qCBA.
Kliegr, Tomáš, Jiří Jarkovský, Helena Jiřincová, Jaroslav Kuchař, Tomáš Karel, David Chudán, Stanislav Vojíř, Michal Zavřel, Ondřej Šanca, and Ruth Tachezy. "Can variants, reinfection, symptoms and test types affect COVID-19 diagnostic performance? A large-scale retrospective study of AG-RDTs during circulation of Delta and Omicron variants, Czechia, December 2021 to February 2022." Eurosurveillance 28, no. 38 (2023): 2200938.
Lukáš Sýkora, Tomáš Kliegr: Apriori Modified for Action Rules Mining. K-CAP 2023: 30-34
Kliegr, Tomáš, Jiří Jarkovský, Helena Jiřincová, Jaroslav Kuchař, Tomáš Karel, and Ruth Tachezy. "Role of population and test characteristics in antigen-based SARS-CoV-2 diagnosis, Czechia, August to November 2021." Eurosurveillance 27, no. 33 (2022): 2200070.
Žárský, Jiří, Gaetan Lopez, and Tomáš Kliegr. "Explainability of Text Clustering Visualizations—Twitter Disinformation Case Study." IEEE Computer Graphics and Applications 42, no. 4 (2022): 8-19.
Beranová, L., Joachimiak, M. P., Kliegr, T., Rabby, G., & Sklenák, V. (2022). Why was this cited? Explainable machine learning applied to COVID-19 research literature. Scientometrics, 127(5), 2313-2349.
Sýkora, Lukáš, Tomáš Kliegr, and Kateřina Hrudková. "High-Utility Action Rules Mining." RuleML+RR Challenge (2022).
Kliegr, Tomáš, Štěpán Bahník, and Johannes Fürnkranz. "A review of possible effects of cognitive biases on interpretation of rule-based machine learning models." Artificial Intelligence (2021): 103458. Open Access https://www.sciencedirect.com/science/article/pii/S0004370221000096
Václav Zeman, Tomáš Kliegr and Vojtěch Svátek. RDFRules: Making RDF Rule Mining Easier and Even More Efficient. Semantic Web Journal. http://www.semantic-web-journal.net/system/files/swj_3. Accepted.
Stanislav Vojíř, Tomáš Kliegr. Editable Machine Learning Models? A rule-based framework for user studies of explainability Journal of Advances in Data Analysis and Classification. Springer. Preprint: https://nb.vse.cz/~klit01/papers/RuleEditor.pdf
HAHSLER, Michael, JOHNSON, Ian, KLIEGR, Tomáš, and KUCHAŘ, Jaroslav. Associative Classification in R: arc, arulesCBA, and rCBA. R Journal. On-line first December 2019
FÜRNKRANZ, Johannes, KLIEGR, Tomáš, and PAULHEIM, Heiko. "On Cognitive Preferences and the Interpretability of Rule-based Models." Machine learning. Springer. On-line first December 2019
KLIEGR, Tomáš, BAHNÍK, Štěpán, FÜRNKRANZ, Johannes. Advances in machine learning for the behavioral sciences. American Behavioral Scientist. Volume: 64 issue: 2, page(s): 145-175, February 1, 2020. https://doi.org/10.1177/0002764219859639
Stanislav Vojíř, Tomáš Kliegr. Editable Machine Learning Models? A rule-based framework for user studies of explainability Journal of Advances in Data Analysis and Classification. Springer. Preprint: https://nb.vse.cz/~klit01/papers/RuleEditor.pdf
KLIEGR, Tomáš, BAHNÍK, Štěpán, FÜRNKRANZ, Johannes. Advances in machine learning for the behavioral sciences. American Behavioral Scientist. Volume: 64 issue: 2, page(s): 145-175, February 1, 2020. https://doi.org/10.1177/0002764219859639
FÜRNKRANZ, Johannes, KLIEGR, Tomáš, and PAULHEIM, Heiko. "On Cognitive Preferences and the Interpretability of Rule-based Models." Machine learning. Springer. On-line first December 2019
FÜRNKRANZ, Johannes, KLIEGR, Tomáš. "The Need for Interpretability Biases." International Symposium on Intelligent Data Analysis. Springer, Cham, 2018. https://link.springer.com/chapter/10.1007/978-3-030-01768-2_2
KLIEGR, Tomáš, BAHNÍK, Štěpán, FÜRNKRANZ, Johannes. A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. Arxiv preprint https://arxiv.org/abs/1804.02969
Václav Zeman, Tomáš Kliegr and Vojtěch Svátek. RDFRules: Making RDF Rule Mining Easier and Even More Efficient http://www.semantic-web-journal.net/system/files/swj2398.pdf Accepted in Semantic Web Journal
HAHSLER, Michael, JOHNSON, Ian, KLIEGR, Tomáš, and KUCHAŘ, Jaroslav. Associative Classification in R: arc, arulesCBA, and rCBA. R Journal. The R Journal (2019) 11:2, pages 254-267.
FILIP, Jiří, and KLIEGR, Tomáš. PyIDS–Python Implementation of Interpretable Decision Sets Algorithm by Lakkaraju et al, 2016. RuleML Challenge (2019). https://github.com/jirifilip/pyIDS . Best RuleML Challenge Award
KLIEGR, Tomáš, KUCHAŘ, Jaroslav. Tuning Hyperparameters of Classification Based on Associations (CBA). ITAT 2019: 9-16. CEUR-WS.
VOJÍŘ, Stanislav, ZEMAN, Václav, KUCHAŘ, Jaroslav, KLIEGR, Tomáš. EasyMiner.eu: Web Framework for Interpretable Machine Learning based on Rules and Frequent Itemsets.Knowledge-Based Systems, Available online 9 March 2018, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2018.03.006.
KLIEGR, Tomáš, Effect of Cognitive Biases On Human Understanding Of Rule-Based Machine Learning Models. Dissertation thesis. October 2017. Queen Mary University London. https://qmro.qmul.ac.uk/xmlui/handle/123456789/31851
KLIEGR, Tomáš. Quantitative CBA: Small and Comprehensible Association Rule Classification Models. Arxiv preprint https://arxiv.org/abs/1711.10166
FÜRNKRANZ, Johannes, KLIEGR, Tomáš: A Brief Overview of Rule Learning. Rule Technologies: Foundations, Tools, and Applications. RuleML 2015. Springer International Publishing, 2015. 54-69. [paper, preprint]
KLIEGR, Tomáš, KUCHAŘ, Jaroslav, SOTTARA, Davide, VOJÍŘ, Stanislav. Learning Business Rules with Association Rule Classifiers. In: Rules on the Web: From Theory to Applications. [online] Praha, 18.08.2014 – 20.08.2014. Springer, 2014, s. 236–250. ISBN 978-3-319-09870-8. [paper, preprint, DEMO, CBA implementation from Jaroslav Kuchař, my CBA implementation]
ŠKRABAL, Radek, ŠIMŮNEK, Milan, VOJÍŘ, Stanislav, HAZUCHA, Andrej, MAREK, Tomáš, CHUDÁN, David, KLIEGR, Tomáš. Association Rule Mining Following the Web Search Paradigm. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML'12). Bristol, 24.09.2012 – 28.09.2012. Berlin : Springer, 2012, s. 808–811. ISBN 978-3-642-33486-3. [paper, preprint, DEMO, code on github].
KLIEGR, Tomáš, SVÁTEK, Vojtěch, RALBOVSKÝ, Martin, ŠIMŮNEK, Milan. SEWEBAR-CMS: semantic analytical report authoring for data mining results. Journal of Intelligent Information Systems (JIIS), Springer. 2010, s. 1–25. ISSN 0925-9902. [enhanced pdf, paper, preprint]
Summary presentation: Interpretability of machine learning
KUCHAŘ, Jaroslav, KLIEGR, Tomáš: InBeat: JavaScript recommender system supporting sensor input and linked data. Knowledge-Based Systems, 2017, ISSN 0950-7051, http://dx.doi.org/10.1016/j.knosys.2017.07.026.
KLIEGR, Tomáš, KUCHAŘ, Jaroslav. Benchmark of Rule-Based Classifiers in the News Recommendation Task. Experimental IR Meets Multilinguality, Multimodality, and Interaction (CLEF 2015). Volume 9283 of the series Lecture Notes in Computer Science. Springer pp 130-141 [paper, preprint]
KLIEGR, Tomáš, KUCHAŘ, Jaroslav. Orwellian Eye - Video recommendation with Microsoft Kinect. In Conference on Prestigious Applications of Intelligent Systems (PAIS'14) collocated with European Conference on Artificial Intelligence (ECAI'14), Prague, Czech Republic, August 2014, IOS Press [live demo, screencast, paper, preprint]
KUCHAŘ, Jaroslav, KLIEGR, Tomáš. Bag-of-Entities text representation for client-side (video) recommender systems. In First Workshop on Recommender Systems for Television and online Video (RecSysTV'14) of ACM RecSys 2014 Foster City, Silicon Valley, USA, 6th-10th October 2014. [preprint]
KUCHAŘ, Jaroslav, KLIEGR, Tomáš. GAIN: web service for user tracking and preference learning – a SMART TV use case. In: Proceedings of the 7th ACM conference on Recommender systems (RecSys '13). Hongkong, 12.10.2013 – 16.10.2013. New York : ACM, 2013, s. 467–468. ISBN 978-1-4503-2409-0. [paper, preprint].
ECKHARDT, Alan, KLIEGR, Tomáš. Preprocessing Algorithm for Handling Non-Monotone Attributes in the UTA method. In: Proceedings of the Preference Learning: Problems and applications in AI workshop @ ECAI 2012. Montpellier, 28.08.2012. Montpellier : ECAI, 2012, s. 28–32. [proceedings, preprint].
KLIEGR, Tomáš. UTA – NM: Explaining Stated Preferences with Additive Non-Monotonic Utility Functions. In: Proceedings of the Preference Learning ECML/PKDD-2009 workshop. Bled, 07.09.2009 – 11.09.2009. Darmstadt : TU, 2009, s. 56–68. [paper, mirror, DEMO].
KLIEGR, Tomáš, ZAMAZAL, Ondřej. Antonyms are similar: Towards paradigmatic association approachto rating similarity in SimLex-999 and WordSim-353. Data & Knowledge Engineering, Volume 115, May 2018, Pages 174-193.
https://doi.org/10.1016/j.datak.2018.03.004. FREE ACCESS LINK VALID UNTIL JULY 2018
KLIEGR, Tomáš., ZAMAZAL, Ondřej. LHD 2.0: A text mining approach to typing entities in knowledge graphs. Journal of Web Semantics, Elsevier, 2016. [paper, preprint, website]
KLIEGR, Tomáš. Linked Hypernyms: Enriching DBpedia with Targeted Hypernym Discovery. Journal of Web Semantics, Elsevier, 2015. [paper, website]
KLIEGR, Tomáš. Unsupervised Entity Classification with Wikipedia and WordNet. Dissertation thesis. University of Economics, Prague. 2012 [text, webpage, Czech overview]
DOJCHINOVSKI, Milan, KLIEGR, Tomáš. Entityclassifier.eu: Real-Time Classification of Entities in Text with Wikipedia. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML'13). Prague, 23.10.2013. Berlín : Springer, 2013, s. 654–658. ISBN 978-3-642-40990-5. [paper, slides, preprint].
KLIEGR, Tomáš, CHANDRAMOULI, K., NEMRAVA, Jan, SVÁTEK, Vojtěch, IZQUIERDO, E. Combining Image Captions and Visual Analysis for Image Concept Classification. In: 9th International Workshop on Multimedia Data Mining (MDM/KDD 2008). Las Vegas, 24.08.2008 – 27.08.2008. Las Vegas : ACM, 2008, s. 8–17. ISBN 978-1-60558-261-0. [paper, preprint].
Full publication list at the UEP profile page, additional papers with full text are on ResearchGate.