2026-04-28T20:47:01Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/84092024-05-10T11:51:23Zcom_10259_4734com_10259_5086com_10259_2604com_10259_4415com_10259_3989com_10259.4_106com_10259_5645col_10259_4735col_10259_4416col_10259_5646

Pacheco Bonrostro, Joaquín 410 600 0000-0002-7462-8680 Sáiz Vázquez, Olalla 886 500 0000-0003-3602-332X Casado Yusta, Silvia 666 600 0000-0002-9663-1557 Ubillos Landa, Silvia 558 600 0000-0003-4852-8018 2024-01-19T13:10:45Z 2024-01-19T13:10:45Z 2023-10 http://hdl.handle.net/10259/8409 10.1038/s41598-023-44437-4 2045-2322 In the design of classification models, irrelevant or noisy features are often generated. In some cases, there may even be negative interactions among features. These weaknesses can degrade the performance of the models. Feature selection is a task that searches for a small subset of relevant features from the original set that generate the most efficient models possible. In addition to improving the efficiency of the models, feature selection confers other advantages, such as greater ease in the generation of the necessary data as well as clearer and more interpretable models. In the case of medical applications, feature selection may help to distinguish which characteristics, habits, and factors have the greatest impact on the onset of diseases. However, feature selection is a complex task due to the large number of possible solutions. In the last few years, methods based on different metaheuristic strategies, mainly evolutionary algorithms, have been proposed. The motivation of this work is to develop a method that outperforms previous methods, with the benefits that this implies especially in the medical field. More precisely, the present study proposes a simple method based on tabu search and multistart techniques. The proposed method was analyzed and compared to other methods by testing their performance on several medical databases. Specifically, eight databases belong to the well-known repository of the University of California in Irvine and one of our own design were used. In these computational tests, the proposed method outperformed other recent methods as gauged by various metrics and classifiers. The analyses were accompanied by statistical tests, the results of which showed that the superiority of our method is significant and therefore strengthened these conclusions. In short, the contribution of this work is the development of a method that, on the one hand, is based on different strategies than those used in recent methods, and on the other hand, improves the performance of these methods. The authors are grateful to the following entities and projects: The Spanish Research Agency (Projects PID2019-104263RB-C44 and PDC2021-121021-C22 and PID2022-139543OB-C44). The Regional Government of “Castilla y León” and FEDER funds (Project BU056P20). application/pdf eng Springer Nature Scientific Reports. 2023, V. 13, n. 1 https://doi.org/10.1038/s41598-023-44437-4 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Economía Medicina Economics Medicine A multistart tabu search-based method for feature selection in medical applications info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Scientific Reports 13 1 THUMBNAIL Pacheco-sr_2023.pdf.jpg Pacheco-sr_2023.pdf.jpg IM Thumbnail image/jpeg 4719 https://riubu.ubu.es/bitstream/10259/8409/4/Pacheco-sr_2023.pdf.jpg c2713b2fc0efc9dd99e919105a72e676 MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 998 https://riubu.ubu.es/bitstream/10259/8409/3/license.txt aa90105e1c90a1b4fe810649aa967b7d MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/8409/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Pacheco-sr_2023.pdf Pacheco-sr_2023.pdf application/pdf 1886782 https://riubu.ubu.es/bitstream/10259/8409/1/Pacheco-sr_2023.pdf eeaee4f31c01fcb8b3ec8b86f34fda1b MD5 1 10259/8409 oai:riubu.ubu.es:10259/8409 2024-05-10 13:51:23.031 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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