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dc.contributor.authorWoloszynski, Tomasz
dc.contributor.authorKurzynski, M.
dc.contributor.authorPodsiadlo, Pawel
dc.contributor.authorStachowiak, Gwidon
dc.date.accessioned2017-01-30T13:56:14Z
dc.date.available2017-01-30T13:56:14Z
dc.date.created2014-03-09T20:00:41Z
dc.date.issued2012
dc.identifier.citationWoloszynski, Tomasz and Kurzynski, Marek and Podsiadlo, Pawel and Stachowiak, Gwidon W. 2012. A measure of competence based on random classification for dynamic ensemble selection. Information Fusion. 13 (3): pp. 207-213.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/36540
dc.identifier.doi10.1016/j.inffus.2011.03.007
dc.description.abstract

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).

dc.publisherElsevier
dc.subjectRandom classification
dc.subjectCompetence measure
dc.subjectMultiple classifier system
dc.subjectDynamic ensemble selection
dc.titleA measure of competence based on random classification for dynamic ensemble selection
dc.typeJournal Article
dcterms.source.volume13
dcterms.source.number3
dcterms.source.startPage207
dcterms.source.endPage213
dcterms.source.issn1566-2535
dcterms.source.titleInformation Fusion
curtin.department
curtin.accessStatusFulltext not available


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