Comparing Rule Mining Approaches for Classification with Reasoning

0494114 - ÚI 2019 RIV DE eng C - Konferenční příspěvek (zahraniční konf.)
Kopp, M. - Bajer, L. - Jílek, M. - Holeňa, Martin
Comparing Rule Mining Approaches for Classification with Reasoning.
ITAT 2018: Information Technologies – Applications and Theory. Proceedings of the 18th conference ITAT 2018. Aachen: Technical University & CreateSpace Independent Publishing Platform, 2018 - (Krajči, S.), s. 52-58. CEUR Workshop Proceedings, V-2203. ISSN 1613-0073.
[ITAT 2018. Conference on Information Technologies – Applications and Theory /18./. Plejsy (SK), 21.09.2018-25.09.2018]
Grant CEP: GA ČR GA17-01251S
Institucionální podpora: RVO:67985807
Klíčová slova: Classification * Comprehensibility * Random Forest * Rule Mining
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
http://ceur-ws.org/Vol-2203/52.pdf

Classification serves an important role in domains such as network security or health care. Although these domains require understanding of the classifier’s decision, there are only a few classification methods trying to justify or explain their results. Classification rules and decision trees are generally considered comprehensible. Therefore, this study compares the classification performance and comprehensibility of a random forest classifier with classification rules extracted by Frequent Item Set Mining, Logical Item Set Mining and by the Explainer algorithm, which was previously proposed by the authors.
Trvalý link: http://hdl.handle.net/11104/0287370