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dc.contributor.authorNematzadeh, Hossein
dc.contributor.authorGarcía-Nieto, José Manuel 
dc.contributor.authorNavas-Delgado, Ismael 
dc.contributor.authorAldana-Montes, José Francisco 
dc.date.accessioned2023-04-19T11:49:52Z
dc.date.available2023-04-19T11:49:52Z
dc.date.created2023-04-19
dc.date.issued2022-10-10
dc.identifier.citationNematzadeh, H., García-Nieto, J., Navas-Delgado, I., & Aldana-Montes, J. F. (2022). Automatic frequency-based feature selection using discrete weighted evolution strategy. Applied Soft Computing, 130, 109699.es_ES
dc.identifier.urihttps://hdl.handle.net/10630/26301
dc.description.abstractHigh dimensional datasets usually suffer from curse of dimensionality which may increase the classification time and decrease the classification accuracy beyond a certain dimensionality. Thus, feature selection is used to discard redundant features for improving classification. Nonetheless, there is not a single feature selection method which could deal with all datasets. Thus, this paper proposes an automatic hybrid feature selection incorporating both filter and wrapper methods called Extended Mutual Congestion-Discrete Weighted Evolution Strategy (EMC-DWES). First, Extended Mutual Congestion (EMC) is proposed as a frequency-based filter ranker to discard irrelevant and redundant features using intrinsic statistics of features. Second, Discrete Weighted Evolution Strategy (DWES) is applied on the remaining features selected by EMC to perform the final automatic feature selection within a wrapper method. DWES clusters the features and applies mutation both to select the most relevant feature in each cluster at a time and to avoid selecting redundant features simultaneously through assigning greater weights to most informative clusters. The performance of EMC-DWES (in maximizing classification accuracy and minimizing the selected subset length) is investigated using benchmark high dimensional medical datasets including Covid-19. Likewise, the superiority of EMC-DWES in comparison with state-of-the-art is also evaluated in all datasets. The implementation of EMC-DWES is available on https://github.com/KhaosResearch/EMC-DWES.es_ES
dc.description.sponsorshipThis work has been partially funded by the Spanish Ministry of Science and Innovation via Grant PID2020-112540RB-C41 (AEI/FEDER, UE) and Andalusian PAIDI program with grant P18-RT-2799. It is also granted by the LifeWatch-ERIC initiative ENVIRONMENTAL AND BIODIVERSITY CLIMATE CHANGE LAB (EnBiC2Lab). Funding for open access charge: Universidad de Málaga / CBUA.es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectAnálisis de datoses_ES
dc.subject.otherCurse of dimensionalityes_ES
dc.subject.otherAutomatic hybrid feature selectiones_ES
dc.subject.otherFilteres_ES
dc.subject.otherWrapperes_ES
dc.subject.otherHigh dimensional medical datasetses_ES
dc.subject.otherCOVID-19es_ES
dc.titleAutomatic frequency-based feature selection using discrete weighted evolution strategyes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Telecomunicaciónes_ES
dc.identifier.doi10.1016/j.asoc.2022.109699
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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