{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:45:03Z","timestamp":1787017503379,"version":"build-2736575974"},"reference-count":62,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61906059 & 62076087"],"award-info":[{"award-number":["61906059 & 62076087"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education of China","award":["IRT17R32"],"award-info":[{"award-number":["IRT17R32"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["JZ2021HGQA0220"],"award-info":[{"award-number":["JZ2021HGQA0220"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2023,2,28]]},"abstract":"<jats:p>\n                    Multi-label learning deals with the problem where an instance is associated with multiple labels simultaneously. Multi-label data is often of high dimensionality and has many noisy, irrelevant, and redundant features. As an important machine learning task, multi-label feature selection has received considerable attention in recent years due to its promising performance in dealing with high-dimensional multi-label data. Existing multi-label feature selection methods typically select the global features which are shared by all instances in a dataset. However, these multi-label feature selection methods may be suboptimal since they do not consider the specific characteristics of instances. In this paper, we propose a novel algorithm that integrates Global and Local Feature Selection (GLFS) to exploit both the global features and a subset of discriminative features shared only locally by a subgroup of instances in a multi-label dataset. Specifically, GLFS employs linear regression and \u2113\n                    <jats:sub>2,1<\/jats:sub>\n                    -norm on the regression parameters to achieve simultaneous global and local feature selection. Moreover, the proposed algorithm has an effective mechanism for utilizing label correlations to improve the feature selection. Experiments on real-world multi-label datasets show the superiority of GLFS over the state-of-the-art multi-label feature selection methods.\n                  <\/jats:p>","DOI":"10.1145\/3532190","type":"journal-article","created":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T07:15:23Z","timestamp":1652166923000},"page":"1-37","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":24,"title":["Integrating Global and Local Feature Selection for Multi-Label Learning"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6383-1683","authenticated-orcid":false,"given":"Zan","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), and School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-1878","authenticated-orcid":false,"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, South Australia, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2396-1704","authenticated-orcid":false,"given":"Xindong","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China), Hefei University of Technology, Hefei, Anhui, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/361573.361582"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btk048"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2004.03.009"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2500491"},{"key":"e_1_3_2_6_2","first-page":"190","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Cabral Ricardo S.","year":"2011","unstructured":"Ricardo S. Cabral, Fernando Torre, Joao P. Costeira, and Alexandre Bernardino. 2011. Matrix completion for multi-label image classification. In Proceedings of the Advances in Neural Information Processing Systems. 190\u2013198."},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-017-0647-y"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5271-6"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2019.10.022"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00532"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/34.400568"},{"key":"e_1_3_2_12_2","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar Janez","year":"2006","unstructured":"Janez Dem\u0161ar. 2006. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7, Jan (2006), 1\u201330.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1961.10482090"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3319911"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.07.022"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2016.2608339"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2018.08.021"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1145\/1754428.1754431"},{"key":"e_1_3_2_19_2","first-page":"1627","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Jian Ling","year":"2016","unstructured":"Ling Jian, Jundong Li, Kai Shu, and Huan Liu. 2016. Multi-label informed feature selection. In Proceedings of the International Joint Conference on Artificial Intelligence. 1627\u20131633."},{"key":"e_1_3_2_20_2","first-page":"1765","volume-title":"Proceedings of the 30th AAAI Conference on Artificial Intelligence","author":"Kong Deguang","year":"2016","unstructured":"Deguang Kong, Ji Liu, Bo Liu, and Xuan Bao. 2016. Uncorrelated group lasso. In Proceedings of the 30th AAAI Conference on Artificial Intelligence. 1765\u20131771."},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCBB.2012.33"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.02.025"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3136625"},{"key":"e_1_3_2_24_2","doi-asserted-by":"crossref","first-page":"835","DOI":"10.18653\/v1\/D15-1099","volume-title":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","author":"Li Li","year":"2015","unstructured":"Li Li, Houfeng Wang, Xu Sun, Baobao Chang, Shi Zhao, and Lei Sha. 2015. Multi-label text categorization with joint learning predictions-as-features method. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 835\u2013839."},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-017-1059-8"},{"issue":"5","key":"e_1_3_2_26_2","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1109\/TCYB.2014.2338893","article-title":"Feature selection in supervised saliency prediction","volume":"45","author":"Liang Ming","year":"2014","unstructured":"Ming Liang and Xiaolin Hu. 2014. Feature selection in supervised saliency prediction. IEEE Transactions on Cybernetics 45, 5 (2014), 914\u2013926.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.06.010"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/TFUZZ.2017.2735947"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080834"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2018.01.022"},{"issue":"6","key":"e_1_3_2_31_2","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.1109\/TCSVT.2018.2848458","article-title":"Implicit and explicit concept relations in deep neural networks for multi-label video\/image annotation","volume":"29","author":"Markatopoulou Foteini","year":"2018","unstructured":"Foteini Markatopoulou, Vasileios Mezaris, and Ioannis Patras. 2018. Implicit and explicit concept relations in deep neural networks for multi-label video\/image annotation. IEEE Transactions on Circuits and Systems for Video Technology 29, 6 (2018), 1631\u20131644.","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"key":"e_1_3_2_32_2","article-title":"Efficient and robust feature selection via joint  \\( \\ell \\) 2, 1-norms minimization","author":"Nie Feiping","year":"2010","unstructured":"Feiping Nie, Heng Huang, Xiao Cai, and Chris Ding. 2010. Efficient and robust feature selection via joint \\( \\ell \\) 2, 1-norms minimization. In Proceeding of Advances in Neural Information Processing Systems. Vol. 23, 1813\u20131821.","journal-title":"Proceeding of Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105285"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2005.159"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/1291233.1291245"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.1242072"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3363573"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-011-5272-5"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007649029923"},{"key":"e_1_3_2_40_2","doi-asserted-by":"crossref","unstructured":"Ying Hu Yong Zhang and Dunwei Gong. 2020. Multiobjective particle swarm optimization for feature selection with fuzzy cost. IEEE Transactions on Cybernetics 51 2 (2020) 874\u2013888.","DOI":"10.1109\/TCYB.2020.3015756"},{"issue":"2","key":"e_1_3_2_41_2","first-page":"67","article-title":"Sur l\u2019\u00e9quation en matrices px= xq","volume":"99","author":"Sylvester James Joseph","year":"1884","unstructured":"James Joseph Sylvester. 1884. Sur l\u2019\u00e9quation en matrices px= xq. Comptes Rendus de l\u2019Acad\u00e9mie des Sciences 99, 2 (1884), 67\u201371.","journal-title":"Comptes Rendus de l\u2019Acad\u00e9mie des Sciences"},{"issue":"4","key":"e_1_3_2_42_2","first-page":"796","article-title":"Effective discriminative feature selection with nontrivial solution","volume":"27","author":"Tao Hong","year":"2015","unstructured":"Hong Tao, Chenping Hou, Feiping Nie, Yuanyuan Jiao, and Dongyun Yi. 2015. Effective discriminative feature selection with nontrivial solution. IEEE Transactions on Neural Networks and Learning Systems 27, 4 (2015), 796\u2013808.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.4018\/jdwm.2007070101"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.2197\/ipsjjip.23.767"},{"issue":"12","key":"e_1_3_2_45_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1471-2105-16-S12-S1","article-title":"Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble","volume":"16","author":"Wang Xiao","year":"2015","unstructured":"Xiao Wang, Jun Zhang, and Guo-Zheng Li. 2015. Multi-location gram-positive and gram-negative bacterial protein subcellular localization using gene ontology and multi-label classifier ensemble. BMC Bioinformatics 16, 12 (2015), 1\u20137.","journal-title":"BMC Bioinformatics"},{"key":"e_1_3_2_46_2","first-page":"5385","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"33","author":"Wei Tong","year":"2019","unstructured":"Tong Wei and Yu-Feng Li. 2019. Learning compact model for large-scale multi-label data. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 5385\u20135392."},{"key":"e_1_3_2_47_2","article-title":"AutoML for multi-label classification: overview and empirical evaluation","author":"Wever Marcel","year":"2021","unstructured":"Marcel Wever, Alexander Tornede, Felix Mohr, and Eyke Hullermeier. 2021. AutoML for multi-label classification: overview and empirical evaluation. IEEE Transactions on Pattern Analysis & Machine Intelligence 43, 9 (2021), 3037\u20133054.","journal-title":"IEEE Transactions on Pattern Analysis & Machine Intelligence"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2014.343"},{"key":"e_1_3_2_49_2","first-page":"3780","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wu Xi-Zhu","year":"2017","unstructured":"Xi-Zhu Wu and Zhi-Hua Zhou. 2017. A unified view of multi-label performance measures. In Proceedings of the International Conference on Machine Learning. PMLR, 3780\u20133788."},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2015.2504420"},{"key":"e_1_3_2_51_2","first-page":"1589","volume-title":"Proceedings of the 22nd International Joint Conference on Artificial Intelligence","author":"Yang Yi","year":"2011","unstructured":"Yi Yang, Heng Tao Shen, Zhigang Ma, Zi Huang, and Xiaofang Zhou. 2011. L2, 1-norm regularized discriminative feature selection for unsupervised. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence. 1589\u20131594."},{"key":"e_1_3_2_52_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106090"},{"key":"e_1_3_2_53_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2013.10.030"},{"key":"e_1_3_2_54_2","first-page":"2512","volume-title":"Proceedings of the 29th International Joint Conference on Artificial Intelligence","author":"Zhang Jia","year":"2020","unstructured":"Jia Zhang, Yidong Lin, Min Jiang, Shaozi Li, Yong Tang, and Kay Chen Tan. 2020. Multi-label feature selection via global relevance and redundancy optimization. In Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2512\u20132518."},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.06.003"},{"key":"e_1_3_2_56_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx833"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2009.06.010"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2006.12.019"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2013.39"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.06.004"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2021.3106975"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2017.09.036"},{"issue":"6","key":"e_1_3_2_63_2","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.1109\/TKDE.2017.2785795","article-title":"Multi-label learning with global and local label correlation","volume":"30","author":"Zhu Yue","year":"2017","unstructured":"Yue Zhu, James T. Kwok, and Zhi-Hua Zhou. 2017. Multi-label learning with global and local label correlation. IEEE Transactions on Knowledge and Data Engineering 30, 6 (2017), 1081\u20131094.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3532190","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3532190","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T14:09:42Z","timestamp":1750169382000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3532190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":62,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,2,28]]}},"alternative-id":["10.1145\/3532190"],"URL":"https:\/\/doi.org\/10.1145\/3532190","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"value":"1556-4681","type":"print"},{"value":"1556-472X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]},"assertion":[{"value":"2021-09-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-04-14","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-02-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}