{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T09:49:03Z","timestamp":1770889743526,"version":"3.50.1"},"reference-count":42,"publisher":"World Scientific Pub Co Pte Ltd","issue":"01","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72104069"],"award-info":[{"award-number":["72104069"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Department of Henan Province, China","award":["222102210065"],"award-info":[{"award-number":["222102210065"]}]},{"name":"Postgraduate Meritocracy Scheme, China","award":["SYL19060145"],"award-info":[{"award-number":["SYL19060145"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Comp. Intel. Appl."],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> Effectively reducing the dimensionality of big data and retaining its key information has been a research challenge. As an important step in data pre-processing, feature selection plays a critical role in reducing data size and increasing the overall value of the data. Many previous studies have focused on single-label feature selection, however, with the increasing variety of data types, the need for feature selection on multi-label data types has also arisen. Unlike single-labeled data, multi-labeled data with more combinations of classifications place higher demands on the capabilities of feature selection algorithms. In this paper, we propose a filter-based Multi-Objective Equilibrium Optimizer algorithm (MOEO-Smp) to solve the feature selection problem for both single-label and multi-label data. MOEO-Smp rates the optimization results of solutions and features based on four pairs of optimization principles, and builds three equilibrium pools to guide exploration and exploitation based on the total scores of solutions and features and the ranking of objective fitness values, respectively. Seven UCI single-label datasets and two Mulan multi-label datasets and one COVID-19 multi-label dataset are used to test the feature selection capability of MOEO-Smp, and the feature selection results are compared with 10 other state-of-the-art algorithms and evaluated using three and seven different metrics, respectively. Feature selection experiments and comparisons with the results in other literatures show that MOEO-Smp not only has the highest classification accuracy and excellent dimensionality reduction on single-labeled data, but also performs better on multi-label data in terms of Hamming loss, accuracy, dimensionality reduction, and so on. <\/jats:p>","DOI":"10.1142\/s1469026823500281","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T07:02:03Z","timestamp":1711090923000},"source":"Crossref","is-referenced-by-count":6,"title":["A Filter-Based Improved Multi-Objective Equilibrium Optimizer for Single-Label and Multi-Label Feature Selection Problem"],"prefix":"10.1142","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8929-4941","authenticated-orcid":false,"given":"Wendong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Business, Henan University, Kaifeng 475004, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9748-6024","authenticated-orcid":false,"given":"Yu","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Management Science and Engineering and School of Business, Henan University, Kaifeng 475004, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2828-4223","authenticated-orcid":false,"given":"Jingsen","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Network Systems and Software School, Henan University Kaifeng 475004, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2785-8141","authenticated-orcid":false,"given":"Huan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Business, Henan University Kaifeng 475004, P.\u00a0R.\u00a0China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2023,11,11]]},"reference":[{"key":"S1469026823500281BIB001","doi-asserted-by":"publisher","DOI":"10.3390\/informatics5010013"},{"key":"S1469026823500281BIB002","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2014.2338893"},{"key":"S1469026823500281BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108701"},{"key":"S1469026823500281BIB004","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"S1469026823500281BIB005","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btk048"},{"key":"S1469026823500281BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/S1088-467X(97)00008-5"},{"key":"S1469026823500281BIB007","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119062"},{"key":"S1469026823500281BIB008","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2021.114778"},{"key":"S1469026823500281BIB009","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3061152"},{"key":"S1469026823500281BIB010","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2919956"},{"key":"S1469026823500281BIB011","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"S1469026823500281BIB012","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.019"},{"key":"S1469026823500281BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygeno.2020.07.027"},{"key":"S1469026823500281BIB014","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106456"},{"key":"S1469026823500281BIB015","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105285"},{"key":"S1469026823500281BIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119303"},{"key":"S1469026823500281BIB017","doi-asserted-by":"publisher","DOI":"10.1016\/j.isatra.2022.06.014"},{"key":"S1469026823500281BIB018","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2022.105795"},{"key":"S1469026823500281BIB019","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2022.104618"},{"key":"S1469026823500281BIB020","doi-asserted-by":"publisher","DOI":"10.1016\/j.compeleceng.2022.108553"},{"key":"S1469026823500281BIB021","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105190"},{"key":"S1469026823500281BIB022","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2022.101222"},{"key":"S1469026823500281BIB024","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-09823-4_34"},{"key":"S1469026823500281BIB028","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.09.063"},{"key":"S1469026823500281BIB029","doi-asserted-by":"publisher","DOI":"10.1109\/4235.996017"},{"key":"S1469026823500281BIB030","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2002.1004388"},{"key":"S1469026823500281BIB031","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2007.892759"},{"key":"S1469026823500281BIB032","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-016-0825-8"},{"key":"S1469026823500281BIB033","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2019.02.028"},{"key":"S1469026823500281BIB034","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-022-07050-7"},{"key":"S1469026823500281BIB035","doi-asserted-by":"publisher","DOI":"10.1016\/j.advengsoft.2017.07.002"},{"key":"S1469026823500281BIB037","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.06.044"},{"key":"S1469026823500281BIB038","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2018.04.018"},{"key":"S1469026823500281BIB039","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"S1469026823500281BIB040","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.05.009"},{"key":"S1469026823500281BIB041","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.02.016"},{"key":"S1469026823500281BIB042","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110305"},{"key":"S1469026823500281BIB043","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109074"},{"key":"S1469026823500281BIB044","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-020-01785-2"},{"key":"S1469026823500281BIB045","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2022.02.024"},{"key":"S1469026823500281BIB046","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2023.109378"},{"key":"S1469026823500281BIB047","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109446"}],"container-title":["International Journal of Computational Intelligence and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S1469026823500281","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T07:03:00Z","timestamp":1711090980000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/10.1142\/S1469026823500281"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,11]]},"references-count":42,"journal-issue":{"issue":"01","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1142\/S1469026823500281"],"URL":"https:\/\/doi.org\/10.1142\/s1469026823500281","relation":{},"ISSN":["1469-0268","1757-5885"],"issn-type":[{"value":"1469-0268","type":"print"},{"value":"1757-5885","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,11]]},"article-number":"2350028"}}