{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T11:32:27Z","timestamp":1776943947003,"version":"3.51.4"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Department of Science and Technology of Jilin Province project","award":["20200403182SF"],"award-info":[{"award-number":["20200403182SF"]}]},{"name":"Department of Science and Technology of Jilin Province project","award":["20210101149JC"],"award-info":[{"award-number":["20210101149JC"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Supercomput"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s11227-024-06714-5","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:06:53Z","timestamp":1733306813000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["An improved conditional relevance and weighted redundancy feature selection method for gene expression data"],"prefix":"10.1007","volume":"81","author":[{"given":"Xiwen","family":"Qin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siqi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaogang","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingru","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liping","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,4]]},"reference":[{"key":"6714_CR1","doi-asserted-by":"publisher","first-page":"103667","DOI":"10.1016\/j.compbiomed.2020.103667","volume":"119","author":"C Li","year":"2020","unstructured":"Li C, Luo X, Qi Y, Gao Z, Lin X (2020) A new feature selection algorithm based on relevance, redundancy and complementarity. Comput Biol Med 119:103667. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103667","journal-title":"Comput Biol Med"},{"issue":"9","key":"6714_CR2","doi-asserted-by":"publisher","first-page":"1747","DOI":"10.1109\/TKDE.2019.2911946","volume":"32","author":"C Tang","year":"2019","unstructured":"Tang C, Liu X, Zhu X, Xiong J, Li M, Xia J, Wang X, Wang L (2019) Feature selective projection with low-rank embedding and dual Laplacian regularization. IEEE Trans Knowl Data Eng 32(9):1747\u20131760. https:\/\/doi.org\/10.1109\/TKDE.2019.2911946","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"13","key":"6714_CR3","doi-asserted-by":"publisher","first-page":"15598","DOI":"10.1007\/s11227-022-04507-2","volume":"78","author":"E Pashaei","year":"2022","unstructured":"Pashaei E, Pashaei E (2022) Hybrid binary arithmetic optimization algorithm with simulated annealing for feature selection in high-dimensional biomedical data. J Supercomput 78(13):15598\u201315637. https:\/\/doi.org\/10.1007\/s11227-022-04507-2","journal-title":"J Supercomput"},{"key":"6714_CR4","doi-asserted-by":"publisher","unstructured":"El Aboudi N, and Benhlima L (2016). Review on wrapper feature selection approaches. In: 2016 International Conference on Engineering & MIS (ICEMIS), pp 1\u20135, IEEE. https:\/\/doi.org\/10.1109\/ICEMIS.2016.7745366.","DOI":"10.1109\/ICEMIS.2016.7745366"},{"key":"6714_CR5","doi-asserted-by":"publisher","unstructured":"Jovi\u0107 A, Brki\u0107 K, and Bogunovi\u0107 N (2015) A review of feature selection methods with applications. In: 2015 38th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 1200\u20131205. IEEE. https:\/\/doi.org\/10.1109\/MIPRO.2015.7160458.","DOI":"10.1109\/MIPRO.2015.7160458"},{"issue":"2","key":"6714_CR6","doi-asserted-by":"publisher","first-page":"e0117988","DOI":"10.1371\/journal.pone.0117988","volume":"10","author":"O Soufan","year":"2015","unstructured":"Soufan O, Kleftogiannis D, Kalnis P, Bajic VB (2015) DWFS: a wrapper feature selection tool based on a parallel genetic algorithm. PLoS ONE 10(2):e0117988. https:\/\/doi.org\/10.1371\/journal.pone.0117988","journal-title":"PLoS ONE"},{"key":"6714_CR7","doi-asserted-by":"publisher","first-page":"114737","DOI":"10.1016\/j.eswa.2021.114737","volume":"175","author":"B Nouri-Moghaddam","year":"2021","unstructured":"Nouri-Moghaddam B, Ghazanfari M, Fathian M (2021) A novel multi-objective forest optimization algorithm for wrapper feature selection. Expert Syst Appl 175:114737. https:\/\/doi.org\/10.1016\/j.eswa.2021.114737","journal-title":"Expert Syst Appl"},{"issue":"5","key":"6714_CR8","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1016\/j.eswa.2013.09.023","volume":"41","author":"D Rodrigues","year":"2014","unstructured":"Rodrigues D, Pereira LA, Nakamura RY, Costa KA, Yang XS, Souza AN, Papa JP (2014) A wrapper approach for feature selection based on bat algorithm and optimum-path forest. Expert Syst Appl 41(5):2250\u20132258. https:\/\/doi.org\/10.1016\/j.eswa.2013.09.023","journal-title":"Expert Syst Appl"},{"key":"6714_CR9","doi-asserted-by":"publisher","first-page":"105349","DOI":"10.1016\/j.compbiomed.2022.105349","volume":"144","author":"R Kundu","year":"2022","unstructured":"Kundu R, Chattopadhyay S, Cuevas E, Sarkar R (2022) AltWOA: altruistic whale optimization algorithm for feature selection on microarray datasets. Comput Biol Med 144:105349. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.105349","journal-title":"Comput Biol Med"},{"key":"6714_CR10","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1007\/978-3-540-35488-8_6","volume-title":"Feature extraction: foundations and applications","author":"TN Lal","year":"2006","unstructured":"Lal TN, Chapelle O, Weston J, Elisseeff A (2006) Embedded methods. Feature extraction: foundations and applications. Springer, Berlin, Heidelberg, pp 137\u2013165"},{"issue":"3","key":"6714_CR11","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1109\/JAS.2019.1911447","volume":"6","author":"H Liu","year":"2019","unstructured":"Liu H, Zhou M, Liu Q (2019) An embedded feature selection method for imbalanced data classification. IEEE\/CAA J Autom Sin 6(3):703\u2013715. https:\/\/doi.org\/10.1109\/JAS.2019.1911447","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"6714_CR12","doi-asserted-by":"publisher","first-page":"13209","DOI":"10.1007\/s00500-020-04734-w","volume":"24","author":"F Coelho","year":"2020","unstructured":"Coelho F, Costa M, Verleysen M, Braga AP (2020) LASSO multi-objective learning algorithm for feature selection. Soft Comput 24:13209\u201313217. https:\/\/doi.org\/10.1007\/s00500-020-04734-w","journal-title":"Soft Comput"},{"key":"6714_CR13","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. https:\/\/doi.org\/10.1016\/j.asoc.2018.02.051","journal-title":"Appl Soft Comput"},{"issue":"14","key":"6714_CR14","doi-asserted-by":"publisher","first-page":"2225","DOI":"10.1016\/j.patrec.2010.03.014","volume":"31","author":"R Genuer","year":"2010","unstructured":"Genuer R, Poggi JM, Tuleau-Malot C (2010) Variable selection using random forests. Pattern Recogn Lett 31(14):2225\u20132236. https:\/\/doi.org\/10.1016\/j.patrec.2010.03.014","journal-title":"Pattern Recogn Lett"},{"key":"6714_CR15","doi-asserted-by":"publisher","unstructured":"S\u00e1nchez-Maro\u00f1o N, Alonso-Betanzos A, and Tombilla-Sanrom\u00e1n M (2007) Filter methods for feature selection\u2013a comparative study. In: International Conference on Intelligent Data Engineering and Automated Learning, pp 178\u2013187. Berlin, Heidelberg: Springer Berlin Heidelberg, https:\/\/doi.org\/10.1007\/978-3-540-77226-2_19.","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"6714_CR16","doi-asserted-by":"publisher","first-page":"106839","DOI":"10.1016\/j.csda.2019.106839","volume":"143","author":"A Bommert","year":"2020","unstructured":"Bommert A, Sun X, Bischl B, Rahnenf\u00fchrer J, Lang M (2020) Benchmark for filter methods for feature selection in high-dimensional classification data. Comput Stat Data Anal 143:106839. https:\/\/doi.org\/10.1016\/j.csda.2019.106839","journal-title":"Comput Stat Data Anal"},{"key":"6714_CR17","unstructured":"Yu, L., & Liu, H. (2003). Feature selection for high-dimensional data: a fast correlation-based filter solution. In: Proceedings of the 20th International Conference on Machine Learning (ICML-03), pp 856\u2013863"},{"key":"6714_CR18","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1016\/j.jbi.2018.07.014","volume":"85","author":"RJ Urbanowicz","year":"2018","unstructured":"Urbanowicz RJ, Meeker M, La Cava W, Olson RS, Moore JH (2018) Relief-based feature selection: introduction and review. J Biomed Inform 85:189\u2013203. https:\/\/doi.org\/10.1016\/j.jbi.2018.07.014","journal-title":"J Biomed Inform"},{"issue":"3","key":"6714_CR19","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1109\/TKDE.2005.39","volume":"17","author":"CT Su","year":"2005","unstructured":"Su CT, Hsu JH (2005) An extended chi2 algorithm for discretization of real value attributes. IEEE Trans Knowl Data Eng 17(3):437\u2013441. https:\/\/doi.org\/10.1109\/TKDE.2005.39","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"6714_CR20","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TNN.2008.2005601","volume":"20","author":"PA Est\u00e9vez","year":"2009","unstructured":"Est\u00e9vez PA, Tesmer M, Perez CA, Zurada JM (2009) Normalized mutual information feature selection. IEEE Trans Neural Networks 20(2):189\u2013201. https:\/\/doi.org\/10.1109\/TNN.2008.2005601","journal-title":"IEEE Trans Neural Networks"},{"issue":"9","key":"6714_CR21","doi-asserted-by":"publisher","first-page":"1535","DOI":"10.3390\/app8091535","volume":"8","author":"F Zhao","year":"2018","unstructured":"Zhao F, Zhao J, Niu X, Luo S, Xin Y (2018) A filter feature selection algorithm based on mutual information for intrusion detection. Appl Sci 8(9):1535. https:\/\/doi.org\/10.3390\/app8091535","journal-title":"Appl Sci"},{"issue":"1","key":"6714_CR22","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1109\/TFUZZ.2020.2989098","volume":"29","author":"L Sun","year":"2020","unstructured":"Sun L, Wang L, Ding W, Qian Y, Xu J (2020) Feature selection using fuzzy neighborhood entropy-based uncertainty measures for fuzzy neighborhood multigranulation rough sets. IEEE Trans Fuzzy Syst 29(1):19\u201333. https:\/\/doi.org\/10.1109\/TFUZZ.2020.2989098","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"6714_CR23","doi-asserted-by":"publisher","first-page":"107064","DOI":"10.1016\/j.asoc.2020.107064","volume":"102","author":"S An","year":"2021","unstructured":"An S, Hu Q, Wang C (2021) Probability granular distance-based fuzzy rough set model. Appl Soft Comput 102:107064. https:\/\/doi.org\/10.1016\/j.asoc.2020.107064","journal-title":"Appl Soft Comput"},{"key":"6714_CR24","doi-asserted-by":"publisher","first-page":"121908","DOI":"10.1016\/j.eswa.2023.121908","volume":"238","author":"Z Huang","year":"2024","unstructured":"Huang Z, Li J (2024) Covering based multi-granulation rough fuzzy sets with applications to feature selection. Expert Syst Appl 238:121908. https:\/\/doi.org\/10.1016\/j.eswa.2023.121908","journal-title":"Expert Syst Appl"},{"issue":"4\u20136","key":"6714_CR25","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1016\/j.neucom.2008.04.005","volume":"72","author":"R Cai","year":"2009","unstructured":"Cai R, Hao Z, Yang X, Wen W (2009) An efficient gene selection algorithm based on mutual information. Neurocomputing 72(4\u20136):991\u2013999. https:\/\/doi.org\/10.1016\/j.neucom.2008.04.005","journal-title":"Neurocomputing"},{"issue":"6","key":"6714_CR26","doi-asserted-by":"publisher","first-page":"1004","DOI":"10.1109\/TCBB.2016.2515582","volume":"13","author":"J Tang","year":"2016","unstructured":"Tang J, Zhou S (2016) A new approach for feature selection from microarray data based on mutual information. IEEE\/ACM Trans Comput Biol Bioinf 13(6):1004\u20131015. https:\/\/doi.org\/10.1109\/TCBB.2016.2515582","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"issue":"3","key":"6714_CR27","doi-asserted-by":"publisher","first-page":"358","DOI":"10.4218\/etrij.2018-0522","volume":"41","author":"DH Mazumder","year":"2019","unstructured":"Mazumder DH, Veilumuthu R (2019) An enhanced feature selection filter for classification of microarray cancer data. ETRI J 41(3):358\u2013370. https:\/\/doi.org\/10.4218\/etrij.2018-0522","journal-title":"ETRI J"},{"issue":"1","key":"6714_CR28","doi-asserted-by":"publisher","first-page":"bbab354","DOI":"10.1093\/bib\/bbab354","volume":"23","author":"A Bommert","year":"2022","unstructured":"Bommert A, Welchowski T, Schmid M, Rahnenf\u00fchrer J (2022) Benchmark of filter methods for feature selection in high-dimensional gene expression survival data. Brief Bioinform 23(1):bbab354. https:\/\/doi.org\/10.1093\/bib\/bbab354","journal-title":"Brief Bioinform"},{"issue":"13","key":"6714_CR29","doi-asserted-by":"publisher","first-page":"2076","DOI":"10.3390\/app14135818","volume":"14","author":"J Zhang","year":"2024","unstructured":"Zhang J, Li S, Yang H, Jiang J, Shi H (2024) Efficient and intelligent feature selection via maximum conditional mutual information for microarray data. Appl Sci 14(13):2076\u20133417. https:\/\/doi.org\/10.3390\/app14135818","journal-title":"Appl Sci"},{"issue":"12","key":"6714_CR30","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1007\/s10462-024-10954-5","volume":"57","author":"YC Wang","year":"2024","unstructured":"Wang YC, Song HM, Wang JS, Song YW, Qi YL, Ma XR (2024) GOG-MBSHO: multi-strategy fusion binary sea-horse optimizer with Gaussian transfer function for feature selection of cancer gene expression data. Artif Intell Rev 57(12):347. https:\/\/doi.org\/10.1007\/s10462-024-10954-5","journal-title":"Artif Intell Rev"},{"key":"6714_CR31","doi-asserted-by":"publisher","first-page":"101941","DOI":"10.1016\/j.artmed.2020.101941","volume":"108","author":"M Abdulla","year":"2020","unstructured":"Abdulla M, Khasawneh MT (2020) G-Forest: an ensemble method for cost-sensitive feature selection in gene expression microarrays. Artif Intell Med 108:101941. https:\/\/doi.org\/10.1016\/j.artmed.2020.101941","journal-title":"Artif Intell Med"},{"key":"6714_CR32","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3326485","author":"S Yang","year":"2023","unstructured":"Yang S, Chen S, Wang P, Chen A, Tian T (2023) Tsplasso: a two-stage prior lasso algorithm for gene selection using omics data. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2023.3326485","journal-title":"IEEE J Biomed Health Inform"},{"key":"6714_CR33","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.neunet.2019.04.015","volume":"117","author":"C Tang","year":"2019","unstructured":"Tang C, Bian M, Liu X, Li M, Zhou H, Wang P, Yin H (2019) Unsupervised feature selection via latent representation learning and manifold regularization. Neural Netw 117:163\u2013178. https:\/\/doi.org\/10.1016\/j.neunet.2019.04.015","journal-title":"Neural Netw"},{"key":"6714_CR34","doi-asserted-by":"publisher","first-page":"109884","DOI":"10.1016\/j.knosys.2022.109884","volume":"256","author":"F Saberi-Movahed","year":"2022","unstructured":"Saberi-Movahed F, Rostami M, Berahmand K, Karami S, Tiwari P, Oussalah M, Band SS (2022) Dual regularized unsupervised feature selection based on matrix factorization and minimum redundancy with application in gene selection. Knowl-Based Syst 256:109884. https:\/\/doi.org\/10.1016\/j.knosys.2022.109884","journal-title":"Knowl-Based Syst"},{"issue":"9","key":"6714_CR35","doi-asserted-by":"publisher","first-page":"101731","DOI":"10.1016\/j.jksuci.2023.101731","volume":"35","author":"Z Xu","year":"2023","unstructured":"Xu Z, Yang F, Wang H, Sun J, Zhu H, Wang S, Zhang Y (2023) CGUFS: a clustering-guided unsupervised feature selection algorithm for gene expression data. J King Saud Univ Comput Inf Sci 35(9):101731. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101731","journal-title":"J King Saud Univ Comput Inf Sci"},{"issue":"4","key":"6714_CR36","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/72.298224","volume":"5","author":"R Battiti","year":"1994","unstructured":"Battiti R (1994) Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw 5(4):537\u2013550. https:\/\/doi.org\/10.1109\/72.298224","journal-title":"IEEE Trans Neural Netw"},{"issue":"8","key":"6714_CR37","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H Peng","year":"2005","unstructured":"Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226\u20131238. https:\/\/doi.org\/10.1109\/TPAMI.2005.159","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6714_CR38","doi-asserted-by":"publisher","unstructured":"Lin D, and Tang X (2006) Conditional infomax learning: an integrated framework for feature extraction and fusion. In: Computer Vision\u2013ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7\u201313, 2006. Proceedings, Part I 9, pp 68\u201382. Springer Berlin Heidelberg, https:\/\/doi.org\/10.1007\/11744023_6.","DOI":"10.1007\/11744023_6"},{"issue":"9","key":"6714_CR39","first-page":"1531","volume":"5","author":"F Fleuret","year":"2004","unstructured":"Fleuret F (2004) Fast binary feature selection with conditional mutual information. J Mach Learn Res 5(9):1531","journal-title":"J Mach Learn Res"},{"key":"6714_CR40","unstructured":"Yang H, and Moody J (1999) Feature selection based on joint mutual information. In: Proceedings of International ICSC Symposium on Advances in Intelligent Data Analysis, Vol. 23. Rochester, NY: Citeseer"},{"issue":"3","key":"6714_CR41","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1109\/JSTSP.2008.923858","volume":"2","author":"PE Meyer","year":"2008","unstructured":"Meyer PE, Schretter C, Bontempi G (2008) Information-theoretic feature selection in microarray data using variable complementarity. IEEE J Sel Topics Signal Process 2(3):261\u2013274. https:\/\/doi.org\/10.1109\/JSTSP.2008.923858","journal-title":"IEEE J Sel Topics Signal Process"},{"issue":"4","key":"6714_CR42","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1109\/TKDE.2017.2650906","volume":"29","author":"J Wang","year":"2017","unstructured":"Wang J, Wei JM, Yang Z, Wang SQ (2017) Feature selection by maximizing independent classification information. IEEE Trans Knowl Data Eng 29(4):828\u2013841. https:\/\/doi.org\/10.1109\/TKDE.2017.2650906","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"6714_CR43","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.patcog.2018.02.020","volume":"79","author":"W Gao","year":"2018","unstructured":"Gao W, Hu L, Zhang P (2018) Class-specific mutual information variation for feature selection. Pattern Recogn 79:328\u2013339. https:\/\/doi.org\/10.1016\/j.patcog.2018.02.020","journal-title":"Pattern Recogn"},{"key":"6714_CR44","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.patrec.2018.06.005","volume":"112","author":"W Gao","year":"2018","unstructured":"Gao W, Hu L, Zhang P, He J (2018) Feature selection considering the composition of feature relevancy. Pattern Recogn Lett 112:70\u201374. https:\/\/doi.org\/10.1016\/j.patrec.2018.06.005","journal-title":"Pattern Recogn Lett"},{"key":"6714_CR45","doi-asserted-by":"publisher","first-page":"1272","DOI":"10.1007\/s10489-019-01597-z","volume":"50","author":"W Gao","year":"2020","unstructured":"Gao W, Hu L, Zhang P (2020) Feature redundancy term variation for mutual information-based feature selection. Appl Intell 50:1272\u20131288. https:\/\/doi.org\/10.1007\/s10489-019-01597-z","journal-title":"Appl Intell"},{"key":"6714_CR46","doi-asserted-by":"publisher","first-page":"4615","DOI":"10.1007\/s10489-018-1239-6","volume":"48","author":"P Zhang","year":"2018","unstructured":"Zhang P, Gao W, Liu G (2018) Feature selection considering weighted relevancy. Appl Intell 48:4615\u20134625. https:\/\/doi.org\/10.1007\/s10489-018-1239-6","journal-title":"Appl Intell"},{"key":"6714_CR47","doi-asserted-by":"publisher","first-page":"3660","DOI":"10.1007\/s10489-020-01726-z","volume":"50","author":"H Zhou","year":"2020","unstructured":"Zhou H, Wen J (2020) Dynamic feature selection method with minimum redundancy information for linear data. Appl Intell 50:3660\u20133677. https:\/\/doi.org\/10.1007\/s10489-020-01726-z","journal-title":"Appl Intell"},{"issue":"2","key":"6714_CR48","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s10044-023-01138-y","volume":"26","author":"K Yin","year":"2023","unstructured":"Yin K, Zhai J, Xie A, Zhu J (2023) Feature selection using max dynamic relevancy and min redundancy. Pattern Anal Appl 26(2):631\u2013643. https:\/\/doi.org\/10.1007\/s10044-023-01138-y","journal-title":"Pattern Anal Appl"},{"issue":"6","key":"6714_CR49","doi-asserted-by":"publisher","first-page":"7175","DOI":"10.1007\/s11063-023-11256-7","volume":"55","author":"L Zhang","year":"2023","unstructured":"Zhang L (2023) A feature selection method using conditional correlation dispersion and redundancy analysis. Neural Process Lett 55(6):7175\u20137209. https:\/\/doi.org\/10.1007\/s11063-023-11256-7","journal-title":"Neural Process Lett"},{"key":"6714_CR50","doi-asserted-by":"publisher","first-page":"106537","DOI":"10.1016\/j.asoc.2020.106537","volume":"95","author":"P Zhang","year":"2020","unstructured":"Zhang P, Gao W (2020) Feature selection considering uncertainty change ratio of the class label. Appl Soft Comput 95:106537. https:\/\/doi.org\/10.1016\/j.asoc.2020.106537","journal-title":"Appl Soft Comput"},{"key":"6714_CR51","doi-asserted-by":"publisher","first-page":"106544","DOI":"10.1016\/j.engappai.2023.106544","volume":"124","author":"S Zhao","year":"2023","unstructured":"Zhao S, Wang M, Ma S, Cui Q (2023) A dynamic support ratio of selected feature-based information for feature selection. Eng Appl Artif Intell 124:106544. https:\/\/doi.org\/10.1016\/j.engappai.2023.106544","journal-title":"Eng Appl Artif Intell"},{"key":"6714_CR52","doi-asserted-by":"publisher","first-page":"109769","DOI":"10.1016\/j.asoc.2022.109769","volume":"131","author":"Z Wang","year":"2022","unstructured":"Wang Z, Chen H, Yuan Z, Yang X, Zhang P, Li T (2022) Exploiting fuzzy rough mutual information for feature selection. Appl Soft Comput 131:109769. https:\/\/doi.org\/10.1016\/j.asoc.2022.109769","journal-title":"Appl Soft Comput"},{"issue":"15","key":"6714_CR53","doi-asserted-by":"publisher","first-page":"18239","DOI":"10.1007\/s10489-022-04445-9","volume":"53","author":"J Xu","year":"2023","unstructured":"Xu J, Meng X, Qu K, Sun Y, Hou Q (2023) Feature selection using relative dependency complement mutual information in fitting fuzzy rough set model. Appl Intell 53(15):18239\u201318262. https:\/\/doi.org\/10.1007\/s10489-022-04445-9","journal-title":"Appl Intell"},{"key":"6714_CR54","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ijar.2021.01.003","volume":"132","author":"OA Salem","year":"2021","unstructured":"Salem OA, Liu F, Chen YPP, Chen X (2021) Feature selection and threshold method based on fuzzy joint mutual information. Int J Approx Reason 132:107\u2013126. https:\/\/doi.org\/10.1016\/j.ijar.2021.01.003","journal-title":"Int J Approx Reason"},{"key":"6714_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J Li","year":"2017","unstructured":"Li J, Cheng K, Wang S, Morstatter F, Trevino RP, Tang J, Liu H (2017) Feature selection: a data perspective. ACM Comput Surv (CSUR) 50:1\u201345. https:\/\/doi.org\/10.1145\/3136625","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"11","key":"6714_CR56","doi-asserted-by":"publisher","first-page":"3236","DOI":"10.1016\/j.patcog.2007.02.007","volume":"40","author":"Z Zhu","year":"2007","unstructured":"Zhu Z, Ong YS, Dash M (2007) Markov blanket-embedded genetic algorithm for gene selection. Pattern Recogn 40(11):3236\u20133248. https:\/\/doi.org\/10.1016\/j.patcog.2007.02.007","journal-title":"Pattern Recogn"},{"key":"6714_CR57","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","volume":"32","author":"M Friedman","year":"1937","unstructured":"Friedman M (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J Am Stat Assoc 32:675\u2013701. https:\/\/doi.org\/10.1080\/01621459.1937.10503522","journal-title":"J Am Stat Assoc"},{"key":"6714_CR58","first-page":"65","volume":"6","author":"S Holm","year":"1979","unstructured":"Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6:65\u201370","journal-title":"Scand J Stat"},{"key":"6714_CR59","doi-asserted-by":"publisher","first-page":"107729","DOI":"10.1016\/j.asoc.2021.107729","volume":"111","author":"G Manikandan","year":"2021","unstructured":"Manikandan G, Abirami S (2021) An efficient feature selection framework based on information theory for high dimensional data. Appl Soft Comput 111:107729. https:\/\/doi.org\/10.1016\/j.asoc.2021.107729","journal-title":"Appl Soft Comput"},{"key":"6714_CR60","doi-asserted-by":"publisher","first-page":"120455","DOI":"10.1016\/j.eswa.2023.120455","volume":"229","author":"X Ma","year":"2023","unstructured":"Ma X, Xu H, Ju C (2023) Class-specific feature selection via maximal dynamic correlation change and minimal redundancy. Expert Syst Appl 229:120455. https:\/\/doi.org\/10.1016\/j.eswa.2023.120455","journal-title":"Expert Syst Appl"},{"key":"6714_CR61","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1023\/A:1025667309714","volume":"53","author":"M Robnik-\u0160ikonja","year":"2003","unstructured":"Robnik-\u0160ikonja M, Kononenko I (2003) Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn 53:23\u201369. https:\/\/doi.org\/10.1023\/A:1025667309714","journal-title":"Mach Learn"},{"issue":"8","key":"6714_CR62","doi-asserted-by":"publisher","first-page":"2886","DOI":"10.1109\/TFUZZ.2021.3096212","volume":"30","author":"J Chen","year":"2021","unstructured":"Chen J, Lin Y, Mi J, Li S, Ding W (2021) A spectral feature selection approach with kernelized fuzzy rough sets. IEEE Trans Fuzzy Syst 30(8):2886\u20132901. https:\/\/doi.org\/10.1109\/TFUZZ.2021.3096212","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"3","key":"6714_CR63","doi-asserted-by":"publisher","first-page":"3514","DOI":"10.1109\/TNNLS.2022.3193929","volume":"35","author":"P Zhang","year":"2022","unstructured":"Zhang P, Li T, Yuan Z, Luo C, Liu K, Yang X (2022) Heterogeneous feature selection based on neighborhood combination entropy. IEEE Trans Neural Netw Learn Syst 35(3):3514\u20133527. https:\/\/doi.org\/10.1109\/TNNLS.2022.3193929","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"7","key":"6714_CR64","doi-asserted-by":"publisher","first-page":"3024","DOI":"10.1109\/TNNLS.2020.3048080","volume":"33","author":"NN Thuy","year":"2021","unstructured":"Thuy NN, Wongthanavasu S (2021) A novel feature selection method for high-dimensional mixed decision tables. IEEE Trans Neural Netw Learn Syst 33(7):3024\u20133037. https:\/\/doi.org\/10.1109\/TNNLS.2020.3048080","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["The Journal of Supercomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06714-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11227-024-06714-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11227-024-06714-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T11:12:45Z","timestamp":1733310765000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11227-024-06714-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["6714"],"URL":"https:\/\/doi.org\/10.1007\/s11227-024-06714-5","relation":{},"ISSN":["0920-8542","1573-0484"],"issn-type":[{"value":"0920-8542","type":"print"},{"value":"1573-0484","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,4]]},"assertion":[{"value":"11 November 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 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":"None.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"238"}}