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dc.contributor.authorPinto, Renê S.
dc.contributor.authorCosta, M. Fernanda P.
dc.contributor.authorCosta, Lino A.
dc.contributor.authorGaspar-Cunha, António
dc.date.accessioned2019-10-15T12:45:02Z
dc.date.available2019-10-15T12:45:02Z
dc.date.issued2019-09
dc.identifier.urihttps://depot.ceon.pl/handle/123456789/17361
dc.description.abstractFeature selection plays a central role in predictive analysis where datasets have hundreds or thousands of variables available. It can also reduce the overall training time and the computational costs of the classifiers used. However, feature selection methods can be computationally intensive or dependent of human expertise to analyze data. This study proposes a neuroevolutionary approach which uses multiobjective evolutionary algorithms to optimize neural network parameters in order to find the best network able to identify the most important variables of analyzed data. Classification is done through a Support Vector Machine (SVM) classifier where specific parameters are also optimized. The method is applied to datasets with different number of features and classes.en
dc.description.sponsorshipEuropean Union
dc.language.isoen
dc.relationMSCA-RISE-2015, NEWEX, No 734205en
dc.rightsUznanie autorstwa 3.0 Polska*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/pl/*
dc.subjectneuroevolutionen
dc.subjectmultiobjective optimizationen
dc.subjectfeature selectionen
dc.titleA neuroevolutionary approach to feature selection using multiobjective evolutionary algorithmsen
dc.typearticleen
dc.contributor.organizationInstitute of Polymers and Composites, University of Minhoen
dc.contributor.organizationCentre of Mathematics, University of Minhoen
dc.contributor.organizationALGORITMI Center, University of Minhoen
dc.contributor.organizationInstitute of Polymers and Composites, University of Minhoen


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Uznanie autorstwa 3.0 Polska
Except where otherwise noted, this item's license is described as Uznanie autorstwa 3.0 Polska