{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T03:21:47Z","timestamp":1777346507665,"version":"3.51.4"},"reference-count":37,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,6,1]],"date-time":"2020-06-01T00:00:00Z","timestamp":1590969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Recent discretization-based feature selection methods show great advantages by introducing the entropy-based cut-points for features to integrate discretization and feature selection into one stage for high-dimensional data. However, current methods usually consider the individual features independently, ignoring the interaction between features with cut-points and those without cut-points, which results in information loss. In this paper, we propose a cooperative coevolutionary algorithm based on the genetic algorithm (GA) and particle swarm optimization (PSO), which searches for the feature subsets with and without entropy-based cut-points simultaneously. For the features with cut-points, a ranking mechanism is used to control the probability of mutation and crossover in GA. In addition, a binary-coded PSO is applied to update the indices of the selected features without cut-points. Experimental results on 10 real datasets verify the effectiveness of our algorithm in classification accuracy compared with several state-of-the-art competitors.<\/jats:p>","DOI":"10.3390\/e22060613","type":"journal-article","created":{"date-parts":[[2020,6,2]],"date-time":"2020-06-02T04:09:03Z","timestamp":1591070943000},"page":"613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Cooperative Coevolutionary Approach to Discretization-Based Feature Selection for High-Dimensional Data"],"prefix":"10.3390","volume":"22","author":[{"given":"Yu","family":"Zhou","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junhao","family":"Kang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8020-6142","authenticated-orcid":false,"given":"Xiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China"},{"name":"Hubei Provincial Engineering Research Center for Intelligent Management of Manufacturing Enterprises, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/S0004-3702(97)00063-5","article-title":"Selection of relevant features and examples in machine learning","volume":"97","author":"Blum","year":"1997","journal-title":"Artif. 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