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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.accessioned2024-08-29T10:22:05Z
dc.date.available2024-08-29T10:22:05Z
dc.date.issued2024
dc.identifier.citationHossein Nematzadeh, José García-Nieto, Ismael Navas-Delgado, José F. Aldana-Montes, Feature selection using a classification error impurity algorithm and an adaptive genetic algorithm improved with an external repository, Knowledge-Based Systems, Volume 301, 2024, 112345, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2024.112345es_ES
dc.identifier.urihttps://hdl.handle.net/10630/32475
dc.description.abstractFeature selection in small-sample high-dimensional datasets enhances classification accuracy and reduces computational time for model training. This paper introduces the filter Classification Error Impurity (CEI) as a frequency-based ranker that improves upon existing methods by better identifying non-linear patterns. According to this, the top features identified by the ensemble of CEI, along with Mutual Information (MI) and Fisher Ratio (FR), form the feature space utilized by the Adaptive Genetic Algorithm with External Repository (AGAwER) to identify the optimal feature combination in a wrapper approach. AGAwER leverages an external repository, incorporating the best solutions to enrich the Genetic Algorithm’s (GA) population, thus promoting diversity and enhancing exploration. As a result, a hybrid method called CMF-AGAwER is proposed, which surpasses existing modern feature selection methods. The implementation and data are accessible on GitHub at https://github.com/KhaosResearch/CMF-AGAwER.es_ES
dc.description.sponsorshipFunding for open access charge: Universidad de Málaga / CBUA. This work has been partially funded by grants (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41, AETHER-UMA(A smart data holistic approach for context-aware data analytics: semantics and context exploitation), and QUAL21 010UMA (Junta de Andalucía).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.subjectAlgoritmos genéticoses_ES
dc.subjectProgramación genética (Informática)es_ES
dc.subjectSistemas expertoses_ES
dc.subjectIngeniería del softwarees_ES
dc.subject.otherFeature selectiones_ES
dc.subject.otherHigh-dimensionales_ES
dc.subject.otherFilteres_ES
dc.subject.otherWrapperes_ES
dc.subject.otherGenetic Algorithmes_ES
dc.titleFeature selection using a classification error impurity algorithm and an adaptive genetic algorithm improved with an external repositoryes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1016/j.knosys.2024.112345
dc.rights.ccAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones_ES


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