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Multiobjective sparse ensemble learning by means of evolutionary algorithms

2018-07Journal article Open access
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dc.contributor.authorZhao, Jiaqi
dc.contributor.authorJiao, Licheng
dc.contributor.authorXia, Shixiong
dc.contributor.authorBasto-Fernandes, Vitor
dc.contributor.authorYevseyeva, Iryna
dc.contributor.authorZhou, Yong
dc.contributor.authorEmmerich, Michael T.M.
dc.date.accessioned2025-06-23T18:18:24Z
dc.date.available2025-06-23T18:18:24Z
dc.date.issued2018-07
dc.description.abstractEnsemble learning can improve the performance of individual classifiers by combining their decisions. The sparseness of ensemble learning has attracted much attention in recent years. In this paper, a novel multiobjective sparse ensemble learning (MOSEL) model is proposed. Firstly, to describe the ensemble classifiers more precisely the detection error trade-off (DET) curve is taken into consideration. The sparsity ratio (sr) is treated as the third objective to be minimized, in addition to false positive rate (fpr) and false negative rate (fnr) minimization. The MOSEL turns out to be augmented DET (ADET) convex hull maximization problem. Secondly, several evolutionary multiobjective algorithms are exploited to find sparse ensemble classifiers with strong performance. The relationship between the sparsity and the performance of ensemble classifiers on the ADET space is explained. Thirdly, an adaptive MOSEL classifiers selection method is designed to select the most suitable ensemble classifiers for a given dataset. The proposed MOSEL method is applied to well-known MNIST datasets and a real-world remote sensing image change detection problem, and several datasets are used to test the performance of the method on this problem. Experimental results based on both MNIST datasets and remote sensing image change detection show that MOSEL performs significantly better than conventional ensemble learning methods.eng
dc.identifier.citationJiaqi Zhao, Licheng Jiao, Shixiong Xia, Vitor Basto Fernandes, Iryna Yevseyeva, Yong Zhou, Michael T.M. Emmerich, Multiobjective sparse ensemble learning by means of evolutionary algorithms, Decision Support Systems, Volume 111, 2018, Pages 86-100, ISSN 0167-9236, https://doi.org/10.1016/j.dss.2018.05.003.
dc.identifier.doi10.1016/j.dss.2018.05.003
dc.identifier.issn0167-9236
dc.identifier.urihttp://hdl.handle.net/10400.8/13390
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier BV
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S016792361830085X
dc.relation.ispartofDecision Support Systems
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectEnsemble learning
dc.subjectChange detection
dc.subjectMultiobjective optimization
dc.subjectClassification
dc.subjectSparse representation
dc.titleMultiobjective sparse ensemble learning by means of evolutionary algorithmseng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage100
oaire.citation.startPage86
oaire.citation.titleDecision Support Systems
oaire.citation.volume111
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameBasto-Fernandes
person.givenNameVitor
person.identifier.ciencia-id581C-52BB-AC4E
person.identifier.orcid0000-0003-4269-5114
person.identifier.ridN-1891-2016
person.identifier.scopus-author-id53363129900
relation.isAuthorOfPublicationfb2d3703-9d6a-4c22-bbc4-9ff14c162feb
relation.isAuthorOfPublication.latestForDiscoveryfb2d3703-9d6a-4c22-bbc4-9ff14c162feb

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