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Classifier Aggregation Using Local Classification Confidence
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SYSNO ASEP 0320925 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Classifier Aggregation Using Local Classification Confidence Title Spojování klasifikátorů pomocí lokální konfidence klasifikace Author(s) Štefka, David (UIVT-O)
Holeňa, Martin (UIVT-O) SAI, RIDSource Title ICAART 2009. - Setúbal : INSTICC, 2009 - ISBN 978-989-8111-66-1 Pages s. 173-178 Number of pages 6 s. Action ICAART 2009. International Conference on Agents and Artificial Intelligence /1./ Event date 19.01.2009-21.01.2009 VEvent location Porto Country PT - Portugal Event type WRD Language eng - English Country PT - Portugal Keywords classifier aggregation ; classifier combining ; classification confidence Subject RIV IN - Informatics, Computer Science R&D Projects 1ET100300517 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR) CEZ AV0Z10300504 - UIVT-O (2005-2011) UT WOS 000267058000026 EID SCOPUS 70349463113 DOI 10.5220/0001545101730178 Annotation Classifier aggregation is a method for improving quality of classification. Instead of using just one classifier, a team of classifiers is created, and the outputs of the individual classifiers are aggregated into the final prediction. In this paper, we study the potential of using measures of local classification confidence in classifier aggregation methods. We introduce four measures of local classification confidence and study their suitability for classifier aggregation. We develop two novel classifier aggregation methods which utilize local classification confidence and we compare them to two commonly used methods for classifier aggregation. The results on four artificial and five real-world benchmark datasets show that by incorporating local classification confidence into classifier aggregation methods, significant improvement in classification quality can be obtained. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2009
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