| A review of possible effects of cognitive biases on interpretation of rule-based machine learning models T Kliegr, Š Bahník, J Fürnkranz Artificial Intelligence 295, 103458, 2021 | 136 | 2021 |
| A brief overview of rule learning J Fürnkranz, T Kliegr International symposium on rules and rule markup languages for the semantic …, 2015 | 107 | 2015 |
| On cognitive preferences and the plausibility of rule-based models J Fürnkranz, T Kliegr, H Paulheim Machine Learning 109 (4), 853-898, 2020 | 89 | 2020 |
| Entityclassifier. eu: real-time classification of entities in text with Wikipedia M Dojchinovski, T Kliegr Machine Learning and Knowledge Discovery in Databases: European Conference …, 2013 | 54 | 2013 |
| Query refinement and user relevance feedback for contextualized image retrieval K Chandramouli, T Kliegr, J Nemrava, V Svátek, E Izquierdo 2008 5th International Conference on Visual Information Engineering (VIE …, 2008 | 51 | 2008 |
| Linked hypernyms: Enriching dbpedia with targeted hypernym discovery T Kliegr Journal of Web Semantics 31, 59-69, 2015 | 49 | 2015 |
| Combining image captions and visual analysis for image concept classification T Kliegr, K Chandramouli, J Nemrava, V Svatek, E Izquierdo Proceedings of the 9th International Workshop on Multimedia Data Mining …, 2008 | 46 | 2008 |
| Learning business rules with association rule classifiers T Kliegr, J Kuchař, D Sottara, S Vojíř Rules on the Web. From Theory to Applications: 8th International Symposium …, 2014 | 38 | 2014 |
| LHD 2.0: A text mining approach to typing entities in knowledge graphs T Kliegr, O Zamazal Journal of Web Semantics 39, 47-61, 2016 | 36 | 2016 |
| EasyMiner. eu: Web framework for interpretable machine learning based on rules and frequent itemsets S Vojíř, V Zeman, J Kuchař, T Kliegr Knowledge-Based Systems 150, 111-115, 2018 | 33 | 2018 |
| Associative Classification in R: arc, arulesCBA, and rCBA. M Hahsler, I Johnson, T Kliegr, J Kucha R Journal 9 (2), 2019 | 24 | 2019 |
| UTA-NM: Explaining stated preferences with additive non-monotonic utility functions T Kliegr Preference Learning 56, 2009 | 23 | 2009 |
| Semantic analytical reports: A framework for post-processing data mining results T Kliegr, M Ralbovský, V Svátek, M Šimůnek, V Jirkovský, J Nemrava, ... Foundations of Intelligent Systems: 18th International Symposium, ISMIS 2009 …, 2009 | 23 | 2009 |
| Advances in machine learning for the behavioral sciences T Kliegr, Š Bahník, J Fürnkranz American Behavioral Scientist 64 (2), 145-175, 2020 | 22 | 2020 |
| Crowdsourced corpus with entity salience annotations M Dojchinovski, D Reddy, T Kliegr, T Vitvar, H Sack Proceedings of the Tenth International Conference on Language Resources and …, 2016 | 19 | 2016 |
| Why was this cited? Explainable machine learning applied to COVID-19 research literature L Beranová, MP Joachimiak, T Kliegr, G Rabby, V Sklenák Scientometrics 127 (5), 2313-2349, 2022 | 18 | 2022 |
| Classification based on associations (CBA)-a performance analysis F Jirı, T Kliegr | 17 | 2018 |
| Benchmark of rule-based classifiers in the news recommendation task T Kliegr, J Kuchař Experimental IR Meets Multilinguality, Multimodality, and Interaction: 6th …, 2015 | 17 | 2015 |
| Antonyms are similar: Towards paradigmatic association approach to rating similarity in SimLex-999 and WordSim-353 T Kliegr, O Zamazal Data & Knowledge Engineering 115, 174-193, 2018 | 15 | 2018 |
| Editable machine learning models? A rule-based framework for user studies of explainability S Vojíř, T Kliegr Advances in Data Analysis and Classification 14 (4), 785-799, 2020 | 14 | 2020 |
