Publication: Multiobjective evolutionary feature selection for fuzzy classification
| dc.contributor.author | Jiménez Barrionuevo, Fernando | |
| dc.contributor.author | Martínez, Carlos | |
| dc.contributor.author | Marzano, Enrico | |
| dc.contributor.author | Palma Méndez, José Tomás | |
| dc.contributor.author | Sánchez Carpena, Gracia | |
| dc.contributor.author | Sciavicco, Guido | |
| dc.contributor.department | Ingeniería de la Información y las Comunicaciones | |
| dc.date.accessioned | 2025-01-08T09:46:32Z | |
| dc.date.available | 2025-01-08T09:46:32Z | |
| dc.date.issued | 2019-05 | |
| dc.description | © 2018 IEEE. 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 Accepted version of a Published Work that appeared in final form in IEEE Transactions on Fuzzy Systems. To access the final edited and published work see https://doi.org/10.1109/TFUZZ.2019.2892363 | |
| dc.description.abstract | The interpretability of classification systems refers to the ability of these to express their behavior in a way that is easily understandable by a user. Interpretable classification models allow for external validation by an expert and, in certain disciplines, such as medicine or business, providing information about decision making is essential for ethical and human reasons. Fuzzy rule based classification systems are consolidated powerful classification tools based on fuzzy logic and designed to produce interpretable models; however, in presence of a large number of attributes, even rule-based models tend to be too complex to be easily interpreted. In this paper, we propose a novel multivariate feature selection method in which both search strategy and classifier are based on multiobjective evolutionary computation. We designed a set of experiments to establish an acceptable setting with respect to the number of evaluations required by the search strategy and by the classifier. We tested our strategy on a real-life dataset and compared the results against a wide range of feature selection methods that includes filter, wrapper, multivariate, and univariate methods, with deterministic and probabilistic search strategies, and with evaluators of diverse nature. Finally, the fuzzy rule based classification model obtained with the proposed method has been evaluated with standard performance metrics and compared with other well-known fuzzy rule based classifiers. We have used two real-life datasets extracted from a contact center; in one case, with the proposed method, we obtained an accuracy of 0.7857 with eight rules, while the best fuzzy classifier compared obtained 0.7679 with eight rules, and in the second case, we obtained an accuracy of 0.7403 with five rules, while the best fuzzy classifier compared obtained 0.6364 with four rules. | es |
| dc.format | application/pdf | es |
| dc.format.extent | 15 | es |
| dc.identifier.citation | IEEE Transactions on Fuzzy Systems, 2019, Vol. 27, Issue 5, pp. 1085 - 1099 | |
| dc.identifier.doi | https://doi.org/10.1109/TFUZZ.2019.2892363 | |
| dc.identifier.issn | Print: 1063-6706 | |
| dc.identifier.issn | Electronic: 1941-0034 | |
| dc.identifier.uri | http://hdl.handle.net/10201/147999 | |
| dc.language | eng | es |
| dc.publisher | Institute of Electrical and Electronics Engineers | es |
| dc.relation | This study was partially supported by computing facilities of Extremadura Research Centre for Advanced Technologies CETA-CIEMAT), funded by the European Regional Development Fund (ERDF). CETA-CIEMAT belongs to CIEMAT and the Government of Spain. This research was also partially supported by Spanish Ministry of Economy and Competitiveness (Spain) under project TIN2013-45491-R. | es |
| dc.relation.publisherversion | https://ieeexplore.ieee.org/document/8607010 | es |
| dc.rights | info:eu-repo/semantics/openAccess | es |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Data classification | es |
| dc.subject | Multi objective evolutionary algorithms | es |
| dc.subject | Feature selection | es |
| dc.subject | Fuzzy rules based learning | es |
| dc.subject.other | CDU::0 - Generalidades.::00 - Ciencia y conocimiento. Investigación. Cultura. Humanidades.::004 - Ciencia y tecnología de los ordenadores. Informática. | es |
| dc.title | Multiobjective evolutionary feature selection for fuzzy classification | es |
| dc.type | info:eu-repo/semantics/article | es |
| dspace.entity.type | Publication | es |
| relation.isAuthorOfPublication | 47706328-5460-433a-b86f-dcbcc9841c53 | |
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| relation.isAuthorOfPublication | 02c0a87a-b9b1-419f-8464-ff56001af67e | |
| relation.isAuthorOfPublication.latestForDiscovery | 47706328-5460-433a-b86f-dcbcc9841c53 |
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