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Fuzzy classification rules based on similarity
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SYSNO ASEP 0384879 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Fuzzy classification rules based on similarity Author(s) Holeňa, Martin (UIVT-O) SAI, RID
Štefka, D. (CZ)Source Title Information Technologies - Applications and Theory. - Seňa : PONT s.r.o., 2012 / Horváth T. - ISBN 978-80-971144-0-4 Pages s. 25-31 Number of pages 7 s. Publication form Online - E Action ITAT 2012. Conference on Theory and Practice of Information Technologies Event date 17.09.2012-21.09.2012 VEvent location Ždiar Country SK - Slovakia Event type EUR Language eng - English Country SK - Slovakia Keywords classification rules ; fuzzy classification ; fuzzy integral ; fuzzy measure ; similarity Subject RIV IN - Informatics, Computer Science R&D Projects GA201/08/0802 GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 EID SCOPUS 84873925550 Annotation The paper deals with the aggregation of classification rules by means of fuzzy integrals, in particular with the fuzzy measures employed in that aggregation. It points out that the kinds of fuzzy measures commonly encountered in this context do not take into account the diversity of classification rules. As a remedy, a new kind of fuzzy measures is proposed, called similarity-aware measures, and several useful properties of such measures are proven. Finally, results of extensive experiments on a number of benchmark datasets are reported, in which a particular similarity-aware measure was applied to a combination of Choquet or Sugeno integrals with three different ways of creating ensembles of classification rules. In the experiments, the new measure was compared with the traditional Sugeno-measure, to which it was clearly superior. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2013
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