{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:11:05Z","timestamp":1710267065903},"reference-count":0,"publisher":"AI Access Foundation","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["jair"],"abstract":"Selecting the best features in a dataset improves accuracy and efficiency of classifiers\u00a0 in a learning process. Datasets generally have more features than necessary, some of\u00a0 them being irrelevant or redundant to others. For this reason, numerous feature selection\u00a0 methods have been developed, in which different evaluation functions and measures are\u00a0 applied. This paper proposes the systematic application of individual feature evaluation\u00a0 methods to initialize search-based feature subset selection methods. An exhaustive review\u00a0 of the starting methods used by genetic algorithms from 2014 to 2020 has been carried out.\u00a0 Subsequently, an in-depth empirical study has been carried out evaluating the proposal for\u00a0 different search-based feature selection methods (Sequential forward and backward selection,\u00a0 Las Vegas filter and wrapper, Simulated Annealing and Genetic Algorithms). Since\u00a0 the computation time is reduced and the classification accuracy with the selected features\u00a0 is improved, the initialization of feature selection proposed in this work is proved to be\u00a0 worth considering while designing any feature selection algorithms.\u00a0<\/jats:p>","DOI":"10.1613\/jair.1.14015","type":"journal-article","created":{"date-parts":[[2022,11,29]],"date-time":"2022-11-29T02:18:14Z","timestamp":1669688294000},"page":"953-983","source":"Crossref","is-referenced-by-count":1,"title":["Initialization of Feature Selection Search for Classification"],"prefix":"10.1613","volume":"75","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-7735-8340","authenticated-orcid":false,"given":"Maria","family":"Luque-Rodriguez","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4865-6538","authenticated-orcid":false,"given":"Jose","family":"Molina-Baena","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-9800-8160","authenticated-orcid":false,"given":"Alfonso","family":"Jimenez-Vilchez","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2486-5792","authenticated-orcid":false,"given":"Antonio","family":"Arauzo-Azofra","sequence":"additional","affiliation":[]}],"member":"16860","published-online":{"date-parts":[[2022,11,27]]},"container-title":["Journal of Artificial Intelligence Research"],"original-title":[],"link":[{"URL":"http:\/\/www.jair.org\/index.php\/jair\/article\/download\/14015\/26865","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.jair.org\/index.php\/jair\/article\/download\/14015\/26865","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T02:18:23Z","timestamp":1669861103000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.jair.org\/index.php\/jair\/article\/view\/14015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,27]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1613\/jair.1.14015","relation":{},"ISSN":["1076-9757"],"issn-type":[{"value":"1076-9757","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,27]]}}}