publications
2021
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.
2020
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
Explainability of Machine Learning: psychological aspects, user studies, reviews
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
Rule learning
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
PREFERENCE LEARNING, RECOMMENDER SYSTEMS, DECISION SUPPORT (highlights)
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].
NATURAL LANGUAGE PROCESSING (highlights)
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.