{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T11:22:51Z","timestamp":1776165771230,"version":"3.50.1"},"reference-count":56,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62337001"],"award-info":[{"award-number":["62337001"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114574","type":"journal-article","created":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T13:16:30Z","timestamp":1774098990000},"page":"114574","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Multi-label feature selection via binary label subspace learning and hypergraph constraints"],"prefix":"10.1016","volume":"174","author":[{"given":"Cong","family":"Guo","sequence":"first","affiliation":[]},{"given":"Huicheng","family":"Zeng","sequence":"additional","affiliation":[]},{"given":"Di","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1371-2608","authenticated-orcid":false,"given":"Changqin","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6084-1851","authenticated-orcid":false,"given":"Xiaodi","family":"Huang","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114574_b1","series-title":"UCI machine learning repository","author":"Asuncion","year":"2007"},{"key":"10.1016\/j.engappai.2026.114574_b2","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1007\/s13042-017-0647-y","article-title":"Multi-label feature selection via feature manifold learning and sparsity regularization","volume":"9","author":"Cai","year":"2018","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.engappai.2026.114574_b3","article-title":"A convex formulation for semi-supervised multi-label feature selection","volume":"vol. 28","author":"Chang","year":"2014"},{"issue":"1","key":"10.1016\/j.engappai.2026.114574_b4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.engappai.2026.114574_b5","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"10.1016\/j.engappai.2026.114574_b6","doi-asserted-by":"crossref","first-page":"4543","DOI":"10.1007\/s10489-021-02550-9","article-title":"A comprehensive survey on feature selection in the various fields of machine learning","volume":"52","author":"Dhal","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.114574_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120662","article-title":"Label relaxation and shared information for multi-label feature selection","volume":"671","author":"Fan","year":"2024","journal-title":"Inform. Sci."},{"key":"10.1016\/j.engappai.2026.114574_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123198","article-title":"Multi-label feature selection with global and local label correlation","volume":"246","author":"Faraji","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"10.1016\/j.engappai.2026.114574_b9","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.1109\/TNNLS.2021.3105142","article-title":"Multilabel feature selection with constrained latent structure shared term","volume":"34","author":"Gao","year":"2021","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106439","article-title":"Relevance assignation feature selection method based on mutual information for machine learning","volume":"209","author":"Gao","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114574_b11","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.asoc.2019.03.041","article-title":"Deep neural network for hierarchical extreme multi-label text classification","volume":"79","author":"Gargiulo","year":"2019","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10.1016\/j.engappai.2026.114574_b12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2716262","article-title":"A tutorial on multilabel learning","volume":"47","author":"Gibaja","year":"2015","journal-title":"ACM Comput. Surv."},{"issue":"7","key":"10.1016\/j.engappai.2026.114574_b13","first-page":"2280","article-title":"Distributed selection of continuous features in multilabel classification using mutual information","volume":"31","author":"Gonz\u00e1lez-L\u00f3pez","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b14","series-title":"Multi-label feature selection based on binary hashing learning and dynamic graph constraints","author":"Guo","year":"2025"},{"key":"10.1016\/j.engappai.2026.114574_b15","doi-asserted-by":"crossref","first-page":"113700","DOI":"10.1016\/j.knosys.2025.113700","article-title":"Multi-label feature selection via exploring reliable instance similarities","author":"Guo","year":"2025","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114574_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.chemolab.2024.105193","article-title":"A novel feature selection framework for incomplete data","volume":"252","author":"Guo","year":"2024","journal-title":"Chemometr. Intell. Lab. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b17","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1016\/j.knosys.2015.06.008","article-title":"Selecting feature subset with sparsity and low redundancy for unsupervised learning","volume":"86","author":"Han","year":"2015","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114574_b18","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125819","article-title":"Enhanced multi-label feature selection considering label-specific relevant information","volume":"264","author":"Han","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.engappai.2026.114574_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.106126","article-title":"Robust multi-label feature selection with dual-graph regularization","volume":"203","author":"Hu","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.engappai.2026.114574_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2020.107344","article-title":"Multi-label feature selection with shared common mode","volume":"104","author":"Hu","year":"2020","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b21","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.neucom.2021.10.022","article-title":"Dynamic subspace dual-graph regularized multi-label feature selection","volume":"467","author":"Hu","year":"2022","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114574_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110583","article-title":"Discriminative label correlation based robust structure learning for multi-label feature selection","volume":"154","author":"Jia","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b23","first-page":"1627","article-title":"Multi-label informed feature selection","volume":"vol. 16","author":"Jian","year":"2016"},{"key":"10.1016\/j.engappai.2026.114574_b24","article-title":"Algorithms for non-negative matrix factorization","volume":"13","author":"Lee","year":"2000","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b25","doi-asserted-by":"crossref","first-page":"1075","DOI":"10.1016\/j.ins.2022.07.154","article-title":"Label correlations variation for robust multi-label feature selection","volume":"609","author":"Li","year":"2022","journal-title":"Inform. Sci."},{"issue":"12","key":"10.1016\/j.engappai.2026.114574_b26","doi-asserted-by":"crossref","first-page":"3343","DOI":"10.1007\/s10115-022-01747-9","article-title":"Robust multi-label feature selection with shared label enhancement","volume":"64","author":"Li","year":"2022","journal-title":"Knowl. Inf. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109120","article-title":"Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation","volume":"134","author":"Li","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108259","article-title":"Learning common and label-specific features for multi-label classification with correlation information","volume":"121","author":"Li","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b29","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102813","article-title":"Fusion-enhanced multi-label feature selection with sparse supplementation","volume":"117","author":"Li","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114574_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.bdr.2023.100383","article-title":"Random manifold sampling and joint sparse regularization for multi-label feature selection","volume":"32","author":"Li","year":"2023","journal-title":"Big Data Res."},{"key":"10.1016\/j.engappai.2026.114574_b31","series-title":"The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices","author":"Lin","year":"2010"},{"key":"10.1016\/j.engappai.2026.114574_b32","series-title":"The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices","author":"Lin","year":"2010"},{"issue":"1","key":"10.1016\/j.engappai.2026.114574_b33","doi-asserted-by":"crossref","first-page":"837","DOI":"10.1007\/s10489-022-03425-3","article-title":"A robust graph based multi-label feature selection considering feature-label dependency","volume":"53","author":"Liu","year":"2023","journal-title":"Appl. Intell."},{"key":"10.1016\/j.engappai.2026.114574_b34","series-title":"Multi-task feature learning via efficient l2, 1-norm minimization","author":"Liu","year":"2012"},{"key":"10.1016\/j.engappai.2026.114574_b35","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.neucom.2016.10.031","article-title":"Hypergraph regularized sparse feature learning","volume":"237","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.engappai.2026.114574_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103088","article-title":"Discriminative approximate regression projection for feature extraction","volume":"120","author":"Liu","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114574_b37","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109629","article-title":"Graph regularized discriminative nonnegative matrix factorization","volume":"139","author":"Liu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114574_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2023.110154","article-title":"Discriminative multi-label feature selection with adaptive graph diffusion","volume":"148","author":"Ma","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b39","article-title":". Efficient and robust feature selection via joint l2, 1-norms minimization","volume":"23","author":"Nie","year":"2010","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.engappai.2026.114574_b40","series-title":"Advances in Computational Intelligence: 15th International Work-Conference on Artificial Neural Networks, IWANN 2019, Gran Canaria, Spain, June 12-14, 2019, Proceedings, Part II 15","first-page":"785","article-title":"Qbso-fs: A reinforcement learning based bee swarm optimization metaheuristic for feature selection","author":"Sadeg","year":"2019"},{"key":"10.1016\/j.engappai.2026.114574_b41","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1109\/TIP.2023.3234497","article-title":"Unsupervised adaptive feature selection with binary hashing","volume":"32","author":"Shi","year":"2023","journal-title":"IEEE Trans. Image Process."},{"key":"10.1016\/j.engappai.2026.114574_b42","series-title":"2013 Brazilian Conference on Intelligent Systems","first-page":"6","article-title":"Relieff for multi-label feature selection","author":"Spola\u00f4r","year":"2013"},{"key":"10.1016\/j.engappai.2026.114574_b43","first-page":"2411","article-title":"Mulan: A java library for multi-label learning","volume":"12","author":"Tsoumakas","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.engappai.2026.114574_b44","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","article-title":"Relief-based feature selection: Introduction and review","volume":"85","author":"Urbanowicz","year":"2018","journal-title":"J. Biomed. Inform."},{"issue":"1","key":"10.1016\/j.engappai.2026.114574_b45","first-page":"3","article-title":"A review of feature selection and its methods","volume":"19","author":"Venkatesh","year":"2019","journal-title":"Cybern. Inf. Technol."},{"key":"10.1016\/j.engappai.2026.114574_b46","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2022.108002","article-title":"Cross-modal fusion for multi-label image classification with attention mechanism","volume":"101","author":"Wang","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.engappai.2026.114574_b47","doi-asserted-by":"crossref","unstructured":"Yang, F., Jia, Y., Liu, H., Dong, Y., Hou, J, 2024. Noisy label removal for partial multi-label learning. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(Pp. pp. 3724\u20133735.","DOI":"10.1145\/3637528.3671677"},{"issue":"4","key":"10.1016\/j.engappai.2026.114574_b48","doi-asserted-by":"crossref","DOI":"10.1016\/j.ipm.2025.104151","article-title":"Multi-label feature selection based on multi-granulation separability","volume":"62","author":"Yao","year":"2025","journal-title":"Inf. Process. Manage."},{"key":"10.1016\/j.engappai.2026.114574_b49","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.patcog.2016.06.009","article-title":"Joint hypergraph learning and sparse regression for feature selection","volume":"63","author":"Zhang","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b50","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110411","article-title":"Multi-label feature selection via latent representation learning and dynamic graph constraints","volume":"151","author":"Zhang","year":"2024","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b51","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","article-title":"Manifold regularized discriminative feature selection for multi-label learning","volume":"95","author":"Zhang","year":"2019","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.engappai.2026.114574_b52","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107924","article-title":"Non-negative multi-label feature selection with dynamic graph constraints","volume":"238","author":"Zhang","year":"2022","journal-title":"Knowl.-Based Syst."},{"issue":"7","key":"10.1016\/j.engappai.2026.114574_b53","doi-asserted-by":"crossref","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","article-title":"ML-KNN: A lazy learning approach to multi-label learning","volume":"40","author":"Zhang","year":"2007","journal-title":"Pattern Recognit."},{"issue":"8","key":"10.1016\/j.engappai.2026.114574_b54","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":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.engappai.2026.114574_b55","article-title":"Learning with hypergraphs: Clustering, classification, and embedding","volume":"19","author":"Zhou","year":"2006","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"6","key":"10.1016\/j.engappai.2026.114574_b56","first-page":"3016","article-title":"Unsupervised spectral feature selection with dynamic hypergraph learning","volume":"34","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Knowl. Data Eng."}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008559?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626008559?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T09:58:57Z","timestamp":1776160737000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626008559"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":56,"alternative-id":["S0952197626008559"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114574","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Multi-label feature selection via binary label subspace learning and hypergraph constraints","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114574","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114574"}}