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dc.contributor.authorZhao, Jiaqien
dc.contributor.authorJiao, Lichengen
dc.contributor.authorXia, Shixongen
dc.contributor.authorBasto-Fernandes, V.en
dc.contributor.authorYevseyeva, Irynaen
dc.contributor.authorZhou, Y.en
dc.contributor.authorEmmerich, Michael T. M.en
dc.date.accessioned2018-07-25T15:18:42Z
dc.date.available2018-07-25T15:18:42Z
dc.date.issued2018-05-31
dc.identifier.citationZhao J., L. Jiao, F. Liu, L. Li, Basto-Fernandes V., Yevseyeva I., Emmerich M.T.M. (2018) Multiobjective Sparse Ensemble Learning by Means of Evolutionary Algorithms, Decision Support Systems, 111, pp. 86-100en
dc.identifier.urihttp://hdl.handle.net/2086/16397
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
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.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectEnsemble learningen
dc.subjectSparse representationen
dc.subjectClassificationen
dc.subjectMultiobjective optimizationen
dc.subjectChange detectionen
dc.titleMultiobjective Sparse Ensemble Learning by Means of Evolutionary Algorithmsen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1016/j.dss.2018.05.003
dc.researchgroupCyber Technology Institute (CTI)en
dc.peerreviewedYesen
dc.explorer.multimediaNoen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2018-05-26en
dc.researchinstituteCyber Technology Institute (CTI)en


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