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Three-objective constrained evolutionary instance selection for classification: Wrapper and filter approaches

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dc.contributor.authorJiménez Barrionuevo, Fernando
dc.contributor.authorSánchez, Gracia
dc.contributor.authorPalma Méndez, José Tomás
dc.contributor.authorSciavicco, Guido
dc.contributor.departmentIngeniería de la Información y las Comunicaciones
dc.date.accessioned2025-01-18T19:00:45Z
dc.date.available2025-01-18T19:00:45Z
dc.date.issued2021-11-11
dc.description© 2021 The Author(s). This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This document is the Published version of a Published Work that appeared in final form in Engineering Applications of Artificial Intelligence. To access the final edited and published work see https://doi.org/10.1016/j.engappai.2021.104531
dc.description.abstractThe large amount of data that is produced today with new technologies is an impediment for machine learning algorithms to work correctly, both due to the memory requirements and the necessary execution times. That is why the processes of reducing both the quantity and the size of the data are increasingly important. One of these processes is the so-called instance selection. In this paper we propose three-objective constrained optimization models to formulate instance selection wrapper and filter methods (separately) for classification problems, which are solved with multi-objective evolutionary algorithms and multi-objective differential evolution. In the proposed instance selection wrapper method, an objective is added to the usual ones to minimize the generalization error of the classifier. The proposed instance selection filter method simultaneously optimizes the correlation, redundancy and consistency of the datasets. Instance retention constraints are imposed on optimization models to retain a maximum percentage of samples, established by the decision maker, in big data scenarios. The experiments have been designed to compare (1) the NSGA-II and MODE algorithms, (2) two- and three-objective optimization models, (3) two different constraint handling techniques, and (4) the proposed evolutionary approaches and other 12 non-evolutionary approaches used in literature. The proposed wrapper and filter instance selection methods have been used in a real-world business engineering application, and have also been validated using three public datasets to facilitate the replicability of the research results. The results of the experiments show the superiority of the three-objective constrained evolutionary techniques proposed in this paper over the non-evolutionary techniques and over the two-objective evolutionary approaches used in the literature.es
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dc.format.extent33es
dc.identifier.citationEngineering Applications of Artificial Intelligence 107 (2022) 104531
dc.identifier.doihttps://doi.org/10.1016/j.engappai.2021.104531
dc.identifier.issnPrint: 0952-1976
dc.identifier.urihttp://hdl.handle.net/10201/148760
dc.languageenges
dc.publisherElsevier, International Federation of Automatic Control (IFAC)es
dc.relationThis work was partially funded by the SITSUS project (Ref: RTI2018-094832-B-I00), given by the Spanish Ministry of Science, Innovation and Universities (MCIU), the Spanish Agency for Research (AEI) and by the European Regional Development Fund (FEDER). This work was supported by the Science and Technology Agency, Séneca Foundation, Comunidad Autónoma de la Región de Murcia, Spain through the research projects 00004/COVI/20 and 00007/COVI/20es
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0952197621003791?via%3Dihub
dc.rightsinfo:eu-repo/semantics/openAccesses
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMulti-objective Evolutionary Algorithmses
dc.subjectMulti-objective constrained optimizationes
dc.subjectInstance Selectiones
dc.subjectClassificationes
dc.subject.otherCDU::0 - Generalidades.::00 - Ciencia y conocimiento. Investigación. Cultura. Humanidades.::004 - Ciencia y tecnología de los ordenadores. Informática.::004.9 - Técnicas basadas en el ordenador orientadas a aplicacioneses
dc.titleThree-objective constrained evolutionary instance selection for classification: Wrapper and filter approacheses
dc.typeinfo:eu-repo/semantics/articlees
dspace.entity.typePublicationes
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