{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:21:36Z","timestamp":1761582096294,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100010023","name":"Natural Science Research of Jiangsu Higher Education Institutions of China","doi-asserted-by":"publisher","award":["19KJA550002"],"award-info":[{"award-number":["19KJA550002"]}],"id":[{"id":"10.13039\/501100010023","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010014","name":"Six Talent Peaks Project in Jiangsu Province","doi-asserted-by":"publisher","award":["XYDXX-054"],"award-info":[{"award-number":["XYDXX-054"]}],"id":[{"id":"10.13039\/501100010014","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1007\/s13042-021-01346-0","type":"journal-article","created":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T19:02:55Z","timestamp":1622314975000},"page":"2639-2657","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A recursive feature retention method for semi-supervised feature selection"],"prefix":"10.1007","volume":"12","author":[{"given":"Qingqing","family":"Pang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7914-0679","authenticated-orcid":false,"given":"Li","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"issue":"4","key":"1346_CR1","doi-asserted-by":"publisher","first-page":"757","DOI":"10.1007\/s13042-017-0760-y","volume":"10","author":"R Shang","year":"2019","unstructured":"Shang R, Chang J, Jiao L et al (2019) Unsupervised feature selection based on self-representation sparse regression and local similarity preserving. Int J Mach Learn Cybern 10(4):757\u2013770","journal-title":"Int J Mach Learn Cybern"},{"issue":"1","key":"1346_CR2","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1007\/s13042-020-01156-w","volume":"12","author":"GN Karagoz","year":"2021","unstructured":"Karagoz GN, Yazici A, D\u00f6keroglu T et al (2021) A new framework of multi-objective evolutionary algorithms for feature selection and multi-label classification of video data. Int J Mach Learn Cybern 12(1):53\u201371","journal-title":"Int J Mach Learn Cybern"},{"issue":"7","key":"1346_CR3","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1007\/s13042-018-0849-y","volume":"10","author":"W Zhang","year":"2019","unstructured":"Zhang W, Kang P, Fang X et al (2019) Joint sparse representation and locality preserving projection for feature extraction. Int J Mach Learn Cybern 10(7):1731\u20131745","journal-title":"Int J Mach Learn Cybern"},{"issue":"11","key":"1346_CR4","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1145\/1968.1972","volume":"27","author":"LG Valiant","year":"1984","unstructured":"Valiant LG (1984) A Theory of the learnable. Commun ACM 27(11):1134\u20131142","journal-title":"Commun ACM"},{"key":"1346_CR5","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher RA (1936) The use of multiple measurements in taxonomic problems. Ann Eugen 7:179\u2013188","journal-title":"Ann Eugen"},{"key":"1346_CR6","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.patcog.2018.06.014","volume":"83","author":"L Smallman","year":"2018","unstructured":"Smallman L, Artemiou A, Morgan J (2018) Sparse generalised principal component analysis. Pattern Recogn 83:443\u2013455","journal-title":"Pattern Recogn"},{"issue":"10","key":"1346_CR7","doi-asserted-by":"publisher","first-page":"1942","DOI":"10.1109\/TNNLS.2013.2297381","volume":"25","author":"Z Lai","year":"2014","unstructured":"Lai Z, Xu Y, Chen Q et al (2014) Multilinear sparse principal component analysis. IEEE Trans Neural Netw Learn Syst 25(10):1942\u20131950","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1346_CR8","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.patcog.2016.02.019","volume":"57","author":"S Wang","year":"2016","unstructured":"Wang S, Lu J, Gu X et al (2016) Semi-supervised linear discriminant analysis for dimension reduction and classification. Pattern Recogn 57:179\u2013189","journal-title":"Pattern Recogn"},{"key":"1346_CR9","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":"1346_CR10","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.patrec.2017.12.022","volume":"102","author":"X Wang","year":"2018","unstructured":"Wang X, Chen RC, Hong C et al (2018) Unsupervised feature analysis with sparse adaptive learning. Pattern Recogn Lett 102:89\u201394","journal-title":"Pattern Recogn Lett"},{"key":"1346_CR11","doi-asserted-by":"crossref","unstructured":"Benabdeslem K, Hindawi M (2011) Constrained Laplacian score for semi-supervised feature selection, in Machine Learning and Knowledge Discovery in Databases, pp 204-218","DOI":"10.1007\/978-3-642-23780-5_23"},{"issue":"9","key":"1346_CR12","doi-asserted-by":"publisher","first-page":"1974","DOI":"10.1109\/TNNLS.2016.2562670","volume":"28","author":"J Xu","year":"2017","unstructured":"Xu J, Tang B, He H et al (2017) Semisupervised feature selection based on relevance and redundancy criteria. IEEE Trans Neural Netw Learn Syst 28(9):1974\u20131984","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"10","key":"1346_CR13","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1016\/j.neucom.2007.06.014","volume":"71","author":"J Zhao","year":"2008","unstructured":"Zhao J, Lu K, He X (2008) Locality sensitive semi-supervised feature selection. Neurocomputing 71(10):1842\u20131848","journal-title":"Neurocomputing"},{"key":"1346_CR14","doi-asserted-by":"crossref","unstructured":"Yang M, Chen Y, Ji G (2010) Semi-Fisher score: a semisupervised method for feature selection. In: International conference on machine learning and cybernetics. IEEE, pp 527\u2013532","DOI":"10.1109\/ICMLC.2010.5581007"},{"key":"1346_CR15","unstructured":"Gu Q, Li Z, Han J (2011) Generalized Fisher score for feature selection. In: Twenty-seventh conference on uncertainty in arti cial intelligence. AUAI Press, pp 266-273"},{"key":"1346_CR16","volume-title":"Neural networks for pattern recognition","author":"CM Bishop","year":"1996","unstructured":"Bishop CM (1996) Neural networks for pattern recognition. Oxford University Press, USA"},{"key":"1346_CR17","unstructured":"Lv S, Jiang H, Zhao L et al. (2013) Manifold based Fisher method for semi-supervised feature selection. In: International conference on fuzzy systems and knowledge discovery. IEEE, pp 664-668"},{"key":"1346_CR18","doi-asserted-by":"publisher","first-page":"8619","DOI":"10.1038\/s41598-018-26806-6","volume":"8","author":"Z Li","year":"2018","unstructured":"Li Z, Liao B, Cai L et al (2018) Semi-supervised maximum discriminative local margin for gene selection. Sci Rep 8:8619","journal-title":"Sci Rep"},{"issue":"2","key":"1346_CR19","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1109\/TKDE.2007.190692","volume":"20","author":"X He","year":"2008","unstructured":"He X, Cai D, Han J (2008) Learning a maximum margin subspace for image retrieval. IEEE Trans Knowl Data Eng 20(2):189\u2013201","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1346_CR20","first-page":"507","volume":"18","author":"X He","year":"2005","unstructured":"He X, Cai D, Niyog P (2005) Laplacian score for feature selection. Neural Inf Process Syst 18:507\u2013514","journal-title":"Neural Inf Process Syst"},{"issue":"8","key":"1346_CR21","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","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1346_CR22","doi-asserted-by":"crossref","unstructured":"Sedgwick P (2012) Pearson\u2019s correlation coefficient, BMJ (online), 345(jul04 1): e4483-e4483","DOI":"10.1136\/bmj.e4483"},{"key":"1346_CR23","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/978-3-030-16145-3_22","volume":"11440","author":"B Tang","year":"2019","unstructured":"Tang B, Zhang L (2019) Multi-class semi-supervised Logistic I-RELIEF feature selection based on nearest neighbor, knowledge discovery and data mining, lecture notes in computer science. Lect Notes Comput Sci 11440:281\u2013292","journal-title":"Lect Notes Comput Sci"},{"issue":"9","key":"1346_CR24","doi-asserted-by":"publisher","first-page":"1610","DOI":"10.1109\/TPAMI.2009.190","volume":"32","author":"Y Sun","year":"2010","unstructured":"Sun Y, Todorovic S, Goodison S (2010) Local learning-based feature selection for high-dimensional data analysis. IEEE Trans Pattern Anal Mach Intell 32(9):1610\u20131626","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1346_CR25","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1016\/j.neucom.2020.02.098","volume":"399","author":"B Tang","year":"2020","unstructured":"Tang B, Zhang L (2020) Local preserving logistic I-relief for semi-supervised feature selection. Neurocomputing 399:48\u201364","journal-title":"Neurocomputing"},{"key":"1346_CR26","doi-asserted-by":"crossref","unstructured":"Zhu L, Miao L, Zhang D (2012) Iterative Laplacian score for feature felection, Chinese conference on pattern recognition, 507\u2013541","DOI":"10.1007\/978-3-642-33506-8_11"},{"issue":"7","key":"1346_CR27","doi-asserted-by":"crossref","first-page":"2986","DOI":"10.1109\/TNNLS.2017.2712823","volume":"29","author":"C Wang","year":"2018","unstructured":"Wang C, Hu Q, Wang X et al (2018) Feature selection based on neighborhood discrimination index. IEEE Trans Neural Netw Learn Syst 29(7):2986\u20132999","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"1346_CR28","unstructured":"Zelnik-manor L, Perona P (2004) Self-tuning spectral clustering. In: Advances in neural information processing systems. Vol 17, MIT Press, Cambridge pp 1601\u20131608"},{"key":"1346_CR29","unstructured":"Dheeru D, Karra Taniskidou E (2017) UCI machine learning repository, UCI machine learning repository. URL http:\/\/archive.ics.uci.edu\/ml"},{"issue":"1\u20132","key":"1346_CR30","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1023\/A:1023949509487","volume":"52","author":"S Monti","year":"2003","unstructured":"Monti S, Tamayo P, Mesirov J et al (2003) Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach Learn 52(1\u20132):91\u2013118","journal-title":"Mach Learn"},{"issue":"5439","key":"1346_CR31","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P et al (1999) Molecular classification of cancer: class discovery and class prediction by gene expression. Science 286(5439):531\u2013537","journal-title":"Science"},{"issue":"7","key":"1346_CR32","doi-asserted-by":"publisher","first-page":"4465","DOI":"10.1073\/pnas.012025199","volume":"99","author":"MP Cooke","year":"2002","unstructured":"Cooke MP, Ching KA, Hakak Y et al (2002) Large-scale analysis of the human and house transcriptomes. Proc Natl Acad Sci 99(7):4465\u20134470","journal-title":"Proc Natl Acad Sci"},{"issue":"2","key":"1346_CR33","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/S1535-6108(02)00032-6","volume":"1","author":"EJ Yeoh","year":"2002","unstructured":"Yeoh EJ, Ross ME, Shurtle SA et al (2002) Classification, subtype discovery, and prediction of outcome in pediatricacute lymphoblastic leukemia by gene expression profiling. Cancer Cell 1(2):133\u2013143","journal-title":"Cancer Cell"},{"issue":"24","key":"1346_CR34","doi-asserted-by":"publisher","first-page":"13790","DOI":"10.1073\/pnas.191502998","volume":"98","author":"A Bhattacharjee","year":"2001","unstructured":"Bhattacharjee A, Richards WG, Staunton J et al (2001) Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinomas sub-classes. Proc Natl Acad Sci 98(24):13790\u201313795","journal-title":"Proc Natl Acad Sci"},{"issue":"6870","key":"1346_CR35","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/415436a","volume":"415","author":"S Pomeroy","year":"2001","unstructured":"Pomeroy S, Tamayo P, Gaasenbeek M et al (2001) Gene expression-based classification and outcome prediction of central nervous system embryonal tumors. Nature 415(6870):436\u2013442","journal-title":"Nature"},{"key":"1346_CR36","volume-title":"Pattern classification","author":"RO Duda","year":"2001","unstructured":"Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, New York","edition":"2"},{"issue":"1\u20132","key":"1346_CR37","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.eswa.2007.07.043","volume":"35","author":"MD Shieh","year":"2008","unstructured":"Shieh MD, Yang CC (2008) Multiclass SVM-REF for product from feature selection. Expert Syst Appl 35(1\u20132):531\u2013541","journal-title":"Expert Syst Appl"},{"issue":"200","key":"1346_CR38","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(200):675\u2013701","journal-title":"J Am Stat Assoc"},{"issue":"293","key":"1346_CR39","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. J Am Stat Assoc 56(293):52\u201364","journal-title":"J Am Stat Assoc"},{"issue":"7","key":"1346_CR40","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1109\/TKDE.2009.62","volume":"21","author":"H Chen","year":"2009","unstructured":"Chen H, Ti\u0148o P, Yao X (2009) Predictive ensemble pruning by expectation propagation. IEEE Trans Knowl Data Eng 21(7):999\u20131013","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1346_CR41","first-page":"1","volume":"181","author":"L Zhang","year":"2019","unstructured":"Zhang L, Huang X, Zhou W (2019) Logistic local hyperplane-Relief: A feature weighting method for classification. Knowl Based Syst 181:1","journal-title":"Knowl Based Syst"},{"issue":"9","key":"1346_CR42","first-page":"338","volume":"81","author":"X Huang","year":"2018","unstructured":"Huang X, Zhang L, Li F et al (2018) Feature weight estimation based on dynamic representation and neighbor sparse reconstruction. Pattern Recogn 81(9):338\u2013403","journal-title":"Pattern Recogn"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01346-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-021-01346-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-021-01346-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T02:07:55Z","timestamp":1672279675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-021-01346-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,29]]},"references-count":42,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["1346"],"URL":"https:\/\/doi.org\/10.1007\/s13042-021-01346-0","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2021,5,29]]},"assertion":[{"value":"14 July 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}