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dc.contributor.authorTsymbal, Alexey
dc.contributor.authorPechenizkiy, Mykola
dc.contributor.authorCunningham, Padraig
dc.date.accessioned2024-10-24T10:17:08Z
dc.date.available2024-10-24T10:17:08Z
dc.date.issued2005
dc.identifier.citationTsymbal, A., Pechenizkiy, M., & Cunningham, P. (2005). Diversity in Search Strategies for Ensemble Feature Selection. <i>Information fusion</i>, <i>6</i>(1), 83-98. <a href="https://doi.org/10.1016/j.inffus.2004.04.003" target="_blank">https://doi.org/10.1016/j.inffus.2004.04.003</a>
dc.identifier.otherCONVID_15086328
dc.identifier.otherTUTKAID_16177
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/97675
dc.description.abstractEnsembles of learnt models constitute one of the main current directions in machine learning and data mining. Ensembles allow us to achieve higher accuracy, which is often not achievable with single models. It was shown theoretically and experimentally that in order for an ensemble to be effective, it should consist of base classifiers that have diversity in their predictions. One technique, which proved to be effective for constructing an ensemble of diverse base classifiers, is the use of different feature subsets, or so-called ensemble feature selection. Many ensemble feature selection strategies incorporate diversity as an objective in the search for the best collection of feature subsets. A number of ways are known to quantify diversity in ensembles of classifiers, and little research has been done about their appropriateness to ensemble feature selection. In this paper, we compare five measures of diversity with regard to their possible use in ensemble feature selection. We conduct experiments on 21 data sets from the UCI machine learning repository, comparing the ensemble accuracy and other characteristics for the ensembles built with ensemble feature selection based on the considered measures of diversity. We consider four search strategies for ensemble feature selection together with the simple random subspacing: genetic search, hill-climbing, and ensemble forward and backward sequential selection. In the experiments, we show that, in some cases, the ensemble feature selection process can be sensitive to the choice of the diversity measure, and that the question of the superiority of a particular measure depends on the context of the use of diversity and on the data being processed. In many cases and on average, the plain disagreement measure is the best. Genetic search, kappa, and dynamic voting with selection form the best combination of a search strategy, diversity measure and integration method.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofseriesInformation fusion
dc.rightsCC BY-NC-ND 4.0
dc.subject.otherEnsemble of classifiers
dc.subject.otherEnsemble diversity
dc.subject.otherFeature selection
dc.subject.otherSearch strategy
dc.subject.otherDynamic integration of classifiers
dc.titleDiversity in Search Strategies for Ensemble Feature Selection
dc.typebookPart
dc.identifier.urnURN:NBN:fi:jyu-202410246531
dc.contributor.laitosTietojenkäsittelytieteiden laitosfi
dc.contributor.laitosDepartment of Computer Science and Information Systemsen
dc.type.urihttp://purl.org/eprint/type/BookItem
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.description.reviewstatuspeerReviewed
dc.format.pagerange83-98
dc.relation.issn1566-2535
dc.relation.numberinseries1
dc.relation.volume6
dc.type.versionacceptedVersion
dc.rights.copyright© 2004 Elsevier B.V. All rights reserved.
dc.rights.accesslevelopenAccessfi
dc.subject.ysotiedonlouhinta
dc.subject.ysostrategiat
dc.subject.ysokoneoppiminen
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p5520
jyx.subject.urihttp://www.yso.fi/onto/yso/p4632
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
dc.rights.urlhttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.relation.doi10.1016/j.inffus.2004.04.003
jyx.fundinginformationThis material is based upon works supported by the Science Foundation Ireland under Grant No. S.F.I.-02IN.1I111. This research is partly supported by the COMAS Graduate School of the University of Jyväskylä, Finland.
dc.type.okmA3


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CC BY-NC-ND 4.0
Except where otherwise noted, this item's license is described as CC BY-NC-ND 4.0