{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T09:57:04Z","timestamp":1781690224002,"version":"3.54.5"},"reference-count":46,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T00:00:00Z","timestamp":1780531200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376114"],"award-info":[{"award-number":["62376114"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372488"],"award-info":[{"award-number":["62372488"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2026J001984"],"award-info":[{"award-number":["2026J001984"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Recently, Multi\u2010label learning has emerged as a crucial technique across various fields. However, it typically requires a large amount of labelled data for effective model training. To address this limitation, many researchers have introduced the label propagation (LP) algorithm from semi\u2010supervised learning into multi\u2010label learning. Unfortunately, conventional LP approaches only transform multi\u2010label problem into single\u2010label formulations, thereby ignoring label correlations. This paper proposes a new LP algorithm integrating fuzzy C\u2010means (FCM) and sorting empowerment for multi\u2010label learning, termed\n                    <jats:bold>C<\/jats:bold>\n                    lustering\u2010based\n                    <jats:bold>S<\/jats:bold>\n                    orting Empowerment\n                    <jats:bold>L<\/jats:bold>\n                    abel\n                    <jats:bold>P<\/jats:bold>\n                    ropagation (CSLP). Firstly, FCM is employed to strengthen the relationship among labels, ensuring that the probability results of the LP algorithm in multi\u2010label scenarios are more consistent with actual data distributions. Afterward, sorting empowerment, inspired by group decision theory, is adopted as an ensemble strategy to enhance the robustness of the LP algorithm. Finally, extensive experiments on seven multi\u2010label datasets demonstrate that CSLP outperforms competing algorithms across four evaluation metrics (Hamming loss, Average precision, Recall score, and MicroF1).\n                  <\/jats:p>","DOI":"10.1111\/exsy.70291","type":"journal-article","created":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T00:28:06Z","timestamp":1780619286000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Ensemble Label Propagation With Fuzzy C\u2010Means and Sorting Empowerment Strategy for Multi\u2010Label Learning"],"prefix":"10.1111","volume":"43","author":[{"given":"Yifeng","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Computer Science Minnan Normal University  Zhangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science Minnan Normal University  Zhangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Science Minnan Normal University  Zhangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Depeng","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence China University of Petroleum  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baoya","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science Minnan Normal University  Zhangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science Minnan Normal University  Zhangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guohe","family":"Li","sequence":"additional","affiliation":[{"name":"Xinjiang Key Laboratory of Intelligent Petroleum Exploration and Engineering China University of Petroleum\u2010Beijing at Karamay  Karamay Xinjiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,4]]},"reference":[{"key":"e_1_2_9_2_1","unstructured":"A. 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