{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:43:14Z","timestamp":1780375394389,"version":"3.54.1"},"reference-count":38,"publisher":"Emerald","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,11]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>As intelligent technology advances, practical applications often involve data with multiple labels. Therefore, multi-label feature selection methods have attracted much attention to extract valuable information. However, current methods tend to lack interpretability when evaluating the relationship between different types of variables without considering the potential causal relationship.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/methodology\/approach<\/jats:title>\n                    <jats:p>To address the above problems, we propose an ensemble causal feature selection method based on mutual information and group fusion strategy (CMIFS) for multi-label data. First, the causal relationship between labels and features is analyzed by local causal structure learning, respectively, to obtain a causal feature set. Second, we eliminate false positive features from the obtained feature set using mutual information to improve the feature subset reliability. Eventually, we employ a group fusion strategy to fuse the obtained feature subsets from multiple data sub-space to enhance the stability of the results.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Findings<\/jats:title>\n                    <jats:p>Experimental comparisons are performed on six\u00a0datasets to validate that our proposal\u00a0can enhance the interpretation and robustness of the model compared with other\u00a0methods in different metrics. Furthermore, the statistical analyses further validate the effectiveness of our approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Originality\/value<\/jats:title>\n                    <jats:p>The present study makes a noteworthy contribution to proposing a causal feature selection approach based on mutual information to obtain an approximate optimal feature subset for multi-label data. Additionally, our proposal adopts the group fusion strategy to guarantee the robustness of the obtained feature subset.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1108\/ijicc-04-2024-0144","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T04:54:59Z","timestamp":1721364899000},"page":"671-704","source":"Crossref","is-referenced-by-count":3,"title":["A novel ensemble causal feature selection approach with mutual information and group fusion strategy for multi-label data"],"prefix":"10.1108","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9884-2481","authenticated-orcid":false,"given":"Yifeng","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Computer Science , , Zhangzhou,","place":["China"]},{"name":"Minnan Normal University , , Zhangzhou,","place":["China"]},{"name":"Fujian Province University Key Laboratory of Data Science and Intelligence Application, , 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Zhangzhou,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-6600-7933","authenticated-orcid":false,"given":"Baoya","family":"Wei","sequence":"additional","affiliation":[{"name":"School of Computer Science , , Zhangzhou,","place":["China"]},{"name":"Minnan Normal University , , Zhangzhou,","place":["China"]},{"name":"Fujian Province University Key Laboratory of Data Science and Intelligence Application, , Zhangzhou,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weishuo","family":"Ren","sequence":"additional","affiliation":[{"name":"School of Computer Science , , Zhangzhou,","place":["China"]},{"name":"Minnan Normal University , , Zhangzhou,","place":["China"]},{"name":"Fujian Province University Key Laboratory of Data Science and Intelligence Application, , 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