{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T07:14:25Z","timestamp":1784272465113,"version":"3.55.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Natural Science Foundation of China key Project: Experimental Study on Lagrangian Turbulence Structure and its Influence on Transport diffusion","award":["11732010"],"award-info":[{"award-number":["11732010"]}]},{"DOI":"10.13039\/501100001809","name":"the Natural Science Foundation of China","doi-asserted-by":"crossref","award":["12202309"],"award-info":[{"award-number":["12202309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s10489-024-06205-3","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T08:01:02Z","timestamp":1735286462000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Sparse low-redundancy multi-label feature selection with adaptive dynamic dual graph constraints"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4812-3339","authenticated-orcid":false,"given":"Yanhong","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianxia","family":"Bai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"6205_CR1","doi-asserted-by":"crossref","unstructured":"Chen ZM, Wei XS, Wang P et al (2019) Multi-label image recognition with graph convolutional networks. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 5177\u20135186","DOI":"10.1109\/CVPR.2019.00532"},{"key":"6205_CR2","doi-asserted-by":"crossref","unstructured":"Chang WC, Jiang D, Yu HF et al (2021) Extreme multi-label learning for semantic matching in product search. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining 2643\u20132651","DOI":"10.1145\/3447548.3467092"},{"key":"6205_CR3","unstructured":"Ye H, Chen Z, Wang DH et al (2020) Pretrained generalized autoregressive model with adaptive probabilistic label clusters for extreme multi-label text classification. International Conference on Machine Learning. PMLR 10809\u201310819"},{"key":"6205_CR4","doi-asserted-by":"publisher","first-page":"1061","DOI":"10.1007\/s10618-021-00743-x","volume":"35","author":"J Huang","year":"2021","unstructured":"Huang J, Xu LC, Qian K et al (2021) Multi-label learning with missing and completely unobserved labels. Data Min Knowl Disc 35:1061\u20131086","journal-title":"Data Min Knowl Disc"},{"key":"6205_CR5","doi-asserted-by":"crossref","unstructured":"Khalid S, Khalil T, Nasreen S (2014) A survey of feature selection and feature extraction techniques in machine learning. 2014 science and information conference. IEEE 372\u2013378","DOI":"10.1109\/SAI.2014.6918213"},{"key":"6205_CR6","unstructured":"Guyon L, Gunn S, Nikravesh M et al (2008) Feature extraction: foundations and applications. vol 207, Springer"},{"issue":"6","key":"6205_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"JD Li","year":"2017","unstructured":"Li JD, Cheng KW, Wang SH et al (2017) Feature selection: A data perspective. ACM computing surveys (CSUR) 50(6):1\u201345","journal-title":"ACM computing surveys (CSUR)"},{"key":"6205_CR8","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.patcog.2016.11.003","volume":"64","author":"R Sheikhpour","year":"2017","unstructured":"Sheikhpour R, Sarram MA, Gharaghani S et al (2017) A survey on semi-supervised feature selection methods. Pattern Recogn 64:141\u2013158","journal-title":"Pattern Recogn"},{"key":"6205_CR9","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.isatra.2018.12.010","volume":"88","author":"CC Ding","year":"2019","unstructured":"Ding CC, Zhao M, Lin J et al (2019) Multi-objective iterative optimization algorithm based optimal wavelet filter selection for multi-fault diagnosis of rolling element bearings. ISA Trans 88:199\u2013215","journal-title":"ISA Trans"},{"key":"6205_CR10","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.engappai.2017.12.014","volume":"70","author":"M Labani","year":"2018","unstructured":"Labani M, Moradi P, Ahmadizar F et al (2018) A novel multivariate filter method for feature selection in text classification problems[J]. Eng Appl Artif Intell 70:25\u201337","journal-title":"Eng Appl Artif Intell"},{"issue":"11","key":"6205_CR11","doi-asserted-by":"publisher","first-page":"5257","DOI":"10.1109\/TIP.2017.2733200","volume":"26","author":"C Yao","year":"2017","unstructured":"Yao C, Liu YF, Jiang B et al (2017) Lle score: a new filter-based unsupervised feature selection method based on nonlinear manifold embedding and its application to image recognition. IEEE Trans Image Process 26(11):5257\u20135269","journal-title":"IEEE Trans Image Process"},{"key":"6205_CR12","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1016\/j.neucom.2019.01.017","volume":"333","author":"J Gonz\u00e1lez","year":"2019","unstructured":"Gonz\u00e1lez J, Ortega J, Damas M et al (2019) A new multi-objective wrapper method for feature selection-accuracy and stability analysis for BCI. Neurocomputing 333:407\u2013418","journal-title":"Neurocomputing"},{"key":"6205_CR13","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1016\/j.asoc.2018.04.033","volume":"69","author":"S Jadhav","year":"2018","unstructured":"Jadhav S, He HM, Jenkins K (2018) Information gain directed genetic algorithm wrapper feature selection for credit rating. Appl Soft Comput 69:541\u2013553","journal-title":"Appl Soft Comput"},{"key":"6205_CR14","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.asoc.2018.02.051","volume":"67","author":"S Maldonado","year":"2018","unstructured":"Maldonado S, L\u00f3pez J (2018) Dealing with high-dimensional class-imbalanced datasets: Embedded feature selection for SVM classification. Appl Soft Comput 67:94\u2013105","journal-title":"Appl Soft Comput"},{"issue":"21","key":"6205_CR15","doi-asserted-by":"publisher","first-page":"3727","DOI":"10.1093\/bioinformatics\/bty429","volume":"34","author":"YC Kong","year":"2018","unstructured":"Kong YC, Yu TW (2018) A graph-embedded deep feedforward network for disease outcome classification and feature selection using gene expression data. Bioinformatics 34(21):3727\u20133737","journal-title":"Bioinformatics"},{"key":"6205_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110411","volume":"151","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Huo W, Tang J (2024) Multi-label feature selection via latent representation learning and dynamic graph constraints. Pattern Recogn 151:110411","journal-title":"Pattern Recogn"},{"key":"6205_CR17","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/j.knosys.2018.10.001","volume":"163","author":"YL Zhang","year":"2019","unstructured":"Zhang YL, Yang Y, Li TR et al (2019) A multitask multiview clustering algorithm in heterogeneous situations based on LLE and LE. Knowl-Based Syst 163:776\u2013786","journal-title":"Knowl-Based Syst"},{"key":"6205_CR18","doi-asserted-by":"crossref","unstructured":"Cai D, Zhang C, He X (2010) Unsupervised feature selection for multi-cluster data. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining 333\u2013342","DOI":"10.1145\/1835804.1835848"},{"key":"6205_CR19","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.1007\/s13042-017-0647-y","volume":"9","author":"ZL Cai","year":"2018","unstructured":"Cai ZL, Zhu W (2018) Multi-label feature selection via feature manifold learning and sparsity regularization. Int J Mach Learn Cybern 9:1321\u20131334","journal-title":"Int J Mach Learn Cybern"},{"key":"6205_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106126","volume":"203","author":"J Hu","year":"2020","unstructured":"Hu J, Li YH, Gao WF et al (2020) Robust multi-label feature selection with dual-graph regularization. Knowl-Based Syst 203:106126","journal-title":"Knowl-Based Syst"},{"key":"6205_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108149","volume":"120","author":"R Huang","year":"2021","unstructured":"Huang R, Wu ZJ (2021) Multi-label feature selection via manifold regularization and dependence maximization. Pattern Recogn 120:108149","journal-title":"Pattern Recogn"},{"issue":"3","key":"6205_CR22","doi-asserted-by":"publisher","first-page":"1253","DOI":"10.1109\/TNNLS.2021.3105142","volume":"34","author":"WF Gao","year":"2023","unstructured":"Gao WF, Li YH, Hu L (2023) Multilabel Feature Selection With Constrained Latent Structure Shared Term. IEEE transactions on neural networks and learning systems 34(3):1253\u20131262","journal-title":"IEEE transactions on neural networks and learning systems"},{"key":"6205_CR23","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.patcog.2019.06.003","volume":"95","author":"J Zhang","year":"2019","unstructured":"Zhang J, Luo ZM, Li CD et al (2019) Manifold regularized discriminative feature selection for multi-label learning. Pattern Recogn 95:136\u2013150","journal-title":"Pattern Recogn"},{"key":"6205_CR24","doi-asserted-by":"crossref","unstructured":"Gretton A, Bousquet O, Smola A et al (2005) Measuring statistical dependence with Hilbert-Schmidt norms. International conference on algorithmic learning theory. Berlin, Heidelberg: Springer Berlin Heidelberg 63\u201377","DOI":"10.1007\/11564089_7"},{"key":"6205_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109074","volume":"134","author":"YH Li","year":"2023","unstructured":"Li YH, Hu L, Gao WF (2023) Multi-label feature selection via robust flexible sparse regularization. Pattern Recogn 134:109074","journal-title":"Pattern Recogn"},{"key":"6205_CR26","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.neucom.2021.10.022","volume":"467","author":"JC Hu","year":"2022","unstructured":"Hu JC, Li YH, Xu GC et al (2022) Dynamic subspace dual-graph regularized multi-label feature selection. Neurocomputing 467:184\u2013196","journal-title":"Neurocomputing"},{"key":"6205_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107924","volume":"238","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Ma YC (2022) Non-negative multi-label feature selection with dynamic graph constraints. Knowl-Based Syst 238:107924","journal-title":"Knowl-Based Syst"},{"issue":"3","key":"6205_CR28","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1007\/s13042-022-01679-4","volume":"14","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Ma YC (2023) Sparse multi-label feature selection via dynamic graph manifold regularization. Int J Mach Learn Cybern 14(3):1021\u20131036","journal-title":"Int J Mach Learn Cybern"},{"key":"6205_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109120","volume":"134","author":"YH Li","year":"2023","unstructured":"Li YH, Hu L, Gao WF (2023) Robust sparse and low-redundancy multi-label feature selection with dynamic local and global structure preservation. Pattern Recogn 134:109120","journal-title":"Pattern Recogn"},{"key":"6205_CR30","doi-asserted-by":"crossref","unstructured":"Wu YH, Bai JX (2024) Sparse low-redundancy multi-label feature selection with constrained laplacian rank. International Journal of Machine Learning and Cybernetics 1\u201318","DOI":"10.1007\/s13042-024-02250-z"},{"key":"6205_CR31","doi-asserted-by":"crossref","unstructured":"Zhang Y, Ma YC, Yang XF (2022) Multi-label feature selection based on logistic regression and manifold learning. Applied Intelligence1\u201318","DOI":"10.20944\/preprints202107.0341.v1"},{"key":"6205_CR32","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.neucom.2015.06.010","volume":"168","author":"YJ Lin","year":"2015","unstructured":"Lin YJ, Hu QH, Liu JH et al (2015) Multi-label feature selection based on max-dependency and min-redundancy. Neurocomputing 168:92\u2013103","journal-title":"Neurocomputing"},{"issue":"3","key":"6205_CR33","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.patrec.2012.10.005","volume":"34","author":"J Lee","year":"2013","unstructured":"Lee J, Kim DW (2013) Feature selection for multi-label classification using multivariate mutual information. Pattern Recogn Lett 34(3):349\u2013357","journal-title":"Pattern Recogn Lett"},{"key":"6205_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106365","volume":"206","author":"A Hashemi","year":"2020","unstructured":"Hashemi A, Dowlatshahi MB, Nezamabadi-Pour H (2020) MFS-MCDM: Multi-label feature selection using multi-criteria decision making. Knowl-Based Syst 206:106365","journal-title":"Knowl-Based Syst"},{"key":"6205_CR35","volume-title":"Spectral graph theory","author":"FR Chung","year":"1997","unstructured":"Chung FR, Graham FC (1997) Spectral graph theory. American Mathematical Society"},{"issue":"11","key":"6205_CR36","doi-asserted-by":"publisher","first-page":"652","DOI":"10.1073\/pnas.35.11.652","volume":"35","author":"K Fan","year":"1949","unstructured":"Fan K (1949) On a theorem of Weyl concerning eigenvalues of linear transformations I. Proc Natl Acad Sci 35(11):652\u2013655","journal-title":"Proc Natl Acad Sci"},{"key":"6205_CR37","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.knosys.2015.06.008","volume":"86","author":"JQ Han","year":"2015","unstructured":"Han JQ, Sun ZY, Hao HW (2015) Selecting feature subset with sparsity and low redundancy for unsupervised learning. Knowl-Based Syst 86:210\u2013223","journal-title":"Knowl-Based Syst"},{"key":"6205_CR38","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.knosys.2018.01.009","volume":"145","author":"C Tang","year":"2018","unstructured":"Tang C, Liu XW, Li MM et al (2018) Robust unsupervised feature selection via dual self-representation and manifold regularization. Knowl-Based Syst 145:109\u2013120","journal-title":"Knowl-Based Syst"},{"key":"6205_CR39","unstructured":"Huang J, Nie FP, Huang H (2015) A new simplex sparse learning model to measure data similarity for clustering. Twenty-fourth international joint conference on artificial intelligence"},{"key":"6205_CR40","doi-asserted-by":"crossref","unstructured":"Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788\u2013791","DOI":"10.1038\/44565"},{"key":"6205_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.106621","volume":"212","author":"YL Fan","year":"2021","unstructured":"Fan YL, Liu JH, Weng W et al (2021) Multi-label feature selection with constraint regression and adaptive spectral graph. Knowl-Based Syst 212:106621","journal-title":"Knowl-Based Syst"},{"key":"6205_CR42","first-page":"2411","volume":"12","author":"G Tsoumakas","year":"2011","unstructured":"Tsoumakas G, Spyromitros-Xioufis E, Vilcek J et al (2011) Mulan: A java library for multi-label learning. The Journal of Machine Learning Research 12:2411\u20132414","journal-title":"The Journal of Machine Learning Research"},{"issue":"7","key":"6205_CR43","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"ML Zhang","year":"2007","unstructured":"Zhang ML, Zhou ZH (2007) ML-KNN: a lazy learning approach to multi-label learning. Pattern Recogn 40(7):2038\u20132048","journal-title":"Pattern Recogn"},{"issue":"1","key":"6205_CR44","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A Comparison of Alternative Tests of Significance for the Problem of m Rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"issue":"293","key":"6205_CR45","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1080\/01621459.1961.10482090","volume":"56","author":"OJ Dunn","year":"1961","unstructured":"Dunn OJ (1961) Multiple Comparisons among Means. Publ Am Stat Assoc 56(293):52\u201364","journal-title":"Publ Am Stat Assoc"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06205-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-024-06205-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-024-06205-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T16:04:56Z","timestamp":1738253096000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-024-06205-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,27]]},"references-count":45,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["6205"],"URL":"https:\/\/doi.org\/10.1007\/s10489-024-06205-3","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,27]]},"assertion":[{"value":"13 December 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest"}}],"article-number":"228"}}