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As a result, a multi-label feature selection method based on feature\u2013label subgraph association with graph representation learning (SAGRL) is proposed to represent the complex correlations of features and labels, especially the relationships between features and labels. Specifically, features and labels are mapped to nodes in the graph structure, and the connections between nodes are established to form feature and label sets, respectively, which increase intra-class correlation and decrease inter-class correlation. Further, feature\u2013label subgraphs are constructed by feature and label sets to provide abundant feature combinations. The relationship between each subgraph is adjusted by graph representation learning, the crucial features in different label sets are selected, and the optimal feature subset is obtained by ranking. Experimental studies on 11 datasets show the superior performance of the proposed method with six evaluation metrics over some state-of-the-art multi-label feature selection methods.<\/jats:p>","DOI":"10.3390\/e26110992","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T09:14:38Z","timestamp":1732094078000},"page":"992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Multi-Label Feature Selection with Feature\u2013Label Subgraph Association and Graph Representation Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Jinghou","family":"Ruan","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Mingwei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"given":"Deqing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6165-2158","authenticated-orcid":false,"given":"Maolin","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Smart City, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Xianjun","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Geosciences, Yangtze University, Wuhan 430100, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7955","DOI":"10.1109\/TPAMI.2021.3119334","article-title":"The emerging trends of multi-label learning","volume":"44","author":"Liu","year":"2021","journal-title":"IEEE Trans. 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