{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:01:00Z","timestamp":1777705260133,"version":"3.51.4"},"reference-count":7,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,1,30]]},"abstract":"<jats:p>Deep neural networks have been adopted in multi-label classification for their excellent performance, however, existing methods fail to comprehensively utilize the high-order correlations between instances and the high-order correlations between labels, and these methods are difficult to deal with label noise effectively. We propose a novel end-to-end deep framework named Robust Fused Hypergraph Neural Networks for Multi-Label Classification (RFHNN), which can effectively utilize the two kinds of high-order correlations and adopt them to mitigate the impact of label noise. In RFHNN, Hypergraph Neural Networks (HNNs) are adopted to mine and utilize the high-order correlations of the instances in the feature space and the label space respectively. The high-order correlations of the instances can not only improve the accuracy of the classification and the discrimination of the proposed model, but also lay the foundation for the subsequent noise correction module. Meanwhile, a hypergraph construction method based on the Apriori algorithm is proposed to realize Hypergraph Neural Networks (HNNs), which can mine robust second-order and high-order label correlations effectively. Effective classifiers are learned based on the correlations between the labels, which will not only improve the accuracy of the model, but can also enhance the subsequent noise correction module. In addition, we have designed a noise correction module in the networks. With the help of the high-order correlations among the instances and the effective classifier, the framework can effectively correct the noise and improve the robustness of the model. Extensive experimental results on datasets demonstrate that our proposed approach is better than the state-of-the-art multi-label classification algorithms. When dealing with the multi-label training datasets with noise in the label space, our proposed method also has great performance.<\/jats:p>","DOI":"10.3233\/jifs-212844","type":"journal-article","created":{"date-parts":[[2022,6,14]],"date-time":"2022-06-14T11:48:40Z","timestamp":1655207320000},"page":"3203-3218","source":"Crossref","is-referenced-by-count":1,"title":["Robust fused hypergraph neural networks for multi-label classification"],"prefix":"10.1177","volume":"44","author":[{"given":"Kaixiang","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mathematical Sciences, Nanjing Normal University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanqi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Nanjing Normal University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computing and Information Technology, University of Wollongong, Wollongong, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"9","key":"10.3233\/JIFS-212844_ref1","doi-asserted-by":"crossref","first-page":"1757","DOI":"10.1016\/j.patcog.2004.03.009","article-title":"Learning multi-labelscene classification","volume":"37","author":"Boutell","year":"2004","journal-title":"Pattern Recognition"},{"issue":"2","key":"10.3233\/JIFS-212844_ref5","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"InternationalJournal of Computer Vision"},{"key":"10.3233\/JIFS-212844_ref29","doi-asserted-by":"crossref","unstructured":"Xu J. , Tian H. , Wang Z. , Wang Y. , Kang W. and Chen F. , Joint inputand output space learning for multi-label image classification,, IEEE Transactions on Multimedia 23 (2020), 1696\u20131707.","DOI":"10.1109\/TMM.2020.3002185"},{"issue":"6","key":"10.3233\/JIFS-212844_ref32","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1109\/TCYB.2019.2894985","article-title":"Hybridnoise-oriented multilabel learning","volume":"50","author":"Zhang","year":"2020","journal-title":"IEEE Transactions onCybernetics"},{"issue":"10","key":"10.3233\/JIFS-212844_ref34","doi-asserted-by":"crossref","first-page":"2155","DOI":"10.1109\/TKDE.2017.2721942","article-title":"Disambiguation-free partial labellearning","volume":"29","author":"Zhang","year":"2017","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"8","key":"10.3233\/JIFS-212844_ref36","doi-asserted-by":"crossref","first-page":"1819","DOI":"10.1109\/TKDE.2013.39","article-title":"A review on multi-label learning algorithms","volume":"26","author":"Zhang","year":"2014","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"6","key":"10.3233\/JIFS-212844_ref41","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","article-title":"Multi-label learning with global andlocal label correlation","volume":"30","author":"Zhu","year":"2018","journal-title":"IEEE Transactions on Knowledge andData Engineering"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-212844","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:21Z","timestamp":1777455801000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-212844"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,30]]},"references-count":7,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jifs-212844","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,30]]}}}