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Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures
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SYSNO ASEP 0442868 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title Dynamic Classifier Aggregation using Interaction-Sensitive Fuzzy Measures Author(s) Štefka, D. (CZ)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title Fuzzy Sets and Systems. - : Elsevier - ISSN 0165-0114
Roč. 270, 1 July (2015), s. 25-52Number of pages 28 s. Language eng - English Country NL - Netherlands Keywords Fuzzy integral ; Fuzzy measure ; Dynamic classifier aggregation Subject RIV IN - Informatics, Computer Science R&D Projects GA13-17187S GA ČR - Czech Science Foundation (CSF) Institutional support UIVT-O - RVO:67985807 UT WOS 000352208900002 EID SCOPUS 84926246510 DOI 10.1016/j.fss.2014.09.005 Annotation In classifier aggregation using fuzzy integral, the performance of the classifier system depends heavily on the choice of the underlying fuzzy measure. However, little attention has been given to the choice of the fuzzy measure in the literature; usually, the Sugeno lambda-measure is used. A weakness of the Sugeno lambda-measure is that it cannot model the interactions between individual classifiers. That motivated us to develop two novel fuzzy measures and a modification of an existing fuzzy measure which are interaction-sensitive, i.e., they model not only the confidences of classifiers, but also their mutual similarities. The properties of the measures are first studied theoretically, and in the experimental section, the performance of the proposed measures is compared to the traditionally used additive measure and Sugeno lambda-measure. Experiments on 23 benchmark datasets and 3 different classifier systems show that the interaction-sensitive fuzzy measures clearly outperform their non-interaction sensitive counterparts. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2016
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