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dc.contributor.authorBonab, H.en_US
dc.contributor.authorCan, Fazlıen_US
dc.date.accessioned2021-03-16T08:52:33Z
dc.date.available2021-03-16T08:52:33Z
dc.date.issued2019
dc.identifier.issn2162-237X
dc.identifier.urihttp://hdl.handle.net/11693/75936
dc.description.abstractThe number of component classifiers chosen for an ensemble greatly impacts the prediction ability. In this paper, we use a geometric framework for a priori determining the ensemble size, which is applicable to most of the existing batch and online ensemble classifiers. There are only a limited number of studies on the ensemble size examining majority voting (MV) and weighted MV (WMV). Almost all of them are designed for batch-mode, hardly addressing online environments. Big data dimensions and resource limitations, in terms of time and memory, make the determination of ensemble size crucial, especially for online environments. For the MV aggregation rule, our framework proves that the more strong components we add to the ensemble, the more accurate predictions we can achieve. For the WMV aggregation rule, our framework proves the existence of an ideal number of components, which is equal to the number of class labels, with the premise that components are completely independent of each other and strong enough. While giving the exact definition for a strong and independent classifier in the context of an ensemble is a challenging task, our proposed geometric framework provides a theoretical explanation of diversity and its impact on the accuracy of predictions. We conduct a series of experimental evaluations to show the practical value of our theorems and existing challenges.en_US
dc.language.isoEnglishen_US
dc.source.titleIEEE Transactions on Neural Networks and Learning Systemsen_US
dc.relation.isversionofhttps://dx.doi.org/10.1109/TNNLS.2018.2886341en_US
dc.subjectData streamen_US
dc.subjectEnsemble cardinalityen_US
dc.subjectEnsemble sizeen_US
dc.subjectLaw of diminishing returnsen_US
dc.subjectMajority voting (MV)en_US
dc.subjectSupervised learningen_US
dc.subjectVoting frameworken_US
dc.subjectWeighted MV (WMV)en_US
dc.titleLess is more: a comprehensive framework for the number of components of ensemble classifiersen_US
dc.typeArticleen_US
dc.departmentDepartment of Computer Engineeringen_US
dc.citation.spage2735en_US
dc.citation.epage2745en_US
dc.citation.volumeNumber30en_US
dc.citation.issueNumber9en_US
dc.identifier.doi10.1109/TNNLS.2018.2886341en_US
dc.publisherIEEEen_US
dc.contributor.bilkentauthorCan, Fazlı


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