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Knowl. Discov. Data"],"published-print":{"date-parts":[[2022,2,28]]},"abstract":"<jats:p>\n            In multi-label learning, label correlations commonly exist in the data. Such correlation not only provides useful information, but also imposes significant challenges for multi-label learning. Recently, label-specific feature embedding has been proposed to explore label-specific features from the training data, and uses feature highly customized to the multi-label set for learning. While such feature embedding methods have demonstrated good performance, the creation of the feature embedding space is only based on a single label, without considering label correlations in the data. In this article, we propose to combine multiple label-specific feature spaces, using label correlation, for multi-label learning. The proposed algorithm,\n            <jats:underline>mu<\/jats:underline>\n            lti-\n            <jats:underline>l<\/jats:underline>\n            abel-specific\n            <jats:underline>f<\/jats:underline>\n            eature space\n            <jats:underline>e<\/jats:underline>\n            nsemble (MULFE), takes consideration label-specific features, label correlation, and weighted ensemble principle to form a learning framework. By conducting clustering analysis on each label\u2019s negative and positive instances, MULFE first creates features customized to each label. After that, MULFE utilizes the label correlation to optimize the margin distribution of the base classifiers which are induced by the related label-specific feature spaces. By combining multiple label-specific features, label correlation based weighting, and ensemble learning, MULFE achieves maximum margin multi-label classification goal through the underlying optimization framework. Empirical studies on 10 public data sets manifest the effectiveness of MULFE.\n          <\/jats:p>","DOI":"10.1145\/3451392","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T21:06:18Z","timestamp":1626815178000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["MULFE: Multi-Label Learning via Label-Specific Feature Space Ensemble"],"prefix":"10.1145","volume":"16","author":[{"given":"Yaojin","family":"Lin","sequence":"first","affiliation":[{"name":"School of Computer Science, Minnan Normal University, Zhangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qinghua","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Tianjin University, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinghua","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Automation, Xiamen University, Xiamen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xingquan","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Computer and Electrical Engineering and Computer Science, Florida Atlantic University, Boca Raton, FL"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology), Ministry of Education, and Mininglamp Academy of Sciences, Mininglamp Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,20]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-015-0841-8"},{"key":"e_1_2_1_2_1","volume-title":"Tsang","author":"An Shuang","year":"2016","unstructured":"Shuang An , Qinghua Hu , Witold Pedrycz , Pengfei Zhu , and Eric C. 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