{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T16:38:31Z","timestamp":1789403911170,"version":"build-2803163510"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci. China Inf. Sci."],"published-print":{"date-parts":[[2020,1]]},"DOI":"10.1007\/s11432-019-2633-y","type":"journal-article","created":{"date-parts":[[2019,12,27]],"date-time":"2019-12-27T11:03:07Z","timestamp":1577444587000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["On some aspects of minimum redundancy maximum relevance feature selection"],"prefix":"10.1007","volume":"63","author":[{"given":"Peter","family":"Bugata","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Drotar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"2633_CR1","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/s10115-017-1059-8","volume":"53","author":"Y Li","year":"2017","unstructured":"Li Y, Li T, Liu H. Recent advances in feature selection and its applications. Knowl Inf Syst, 2017, 53:551\u2013577","journal-title":"Knowl Inf Syst"},{"key":"2633_CR2","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1109\/MIS.2017.38","volume":"32","author":"J D Li","year":"2017","unstructured":"Li J D, Liu H. Challenges of feature selection for big data analytics. IEEE Intell Syst, 2017, 32:9\u201315","journal-title":"IEEE Intell Syst"},{"key":"2633_CR3","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.knosys.2015.05.014","volume":"86","author":"V Bol\u00f3on-Canedo","year":"2015","unstructured":"Bol\u00f3on-Canedo V, S\u00e1anchez-Maro\u00f1no N, Alonso-Betanzos A. Recent advances and emerging challenges of feature selection in the context of big data. Knowledge-Based Syst, 2015, 86:33\u201345","journal-title":"Knowledge-Based Syst"},{"key":"2633_CR4","doi-asserted-by":"publisher","first-page":"971","DOI":"10.1109\/TCBB.2015.2478454","volume":"13","author":"J C Ang","year":"2016","unstructured":"Ang J C, Mirzal A, Haron H, et al. Supervised, unsupervised, and semi-supervised feature selection:a review on gene selection. IEEE\/ACM Trans Comput Biol Bioinf, 2016, 13:971\u2013989","journal-title":"IEEE\/ACM Trans Comput Biol Bioinf"},{"key":"2633_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3136625","volume":"50","author":"J D Li","year":"2018","unstructured":"Li J D, Cheng K W, Wang S H, et al. Feature selection. ACM Comput Surv, 2018, 50:1\u201345","journal-title":"ACM Comput Surv"},{"key":"2633_CR6","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1109\/72.298224","volume":"5","author":"R Battiti","year":"1994","unstructured":"Battiti R. Using mutual information for selecting features in supervised neural net learning. IEEE Trans Neural Netw, 1994, 5:537\u2013550","journal-title":"IEEE Trans Neural Netw"},{"key":"2633_CR7","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1109\/72.977291","volume":"13","author":"N Kwak","year":"2002","unstructured":"Kwak N, Choi C H. Input feature selection for classification problems. IEEE Trans Neural Netw, 2002, 13:143\u2013159","journal-title":"IEEE Trans Neural Netw"},{"key":"2633_CR8","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1016\/j.neucom.2008.04.005","volume":"72","author":"R C Cai","year":"2009","unstructured":"Cai R C, Hao Z F, Yang X W, et al. An efficient gene selection algorithm based on mutual information. Neurocomputing, 2009, 72:991\u2013999","journal-title":"Neurocomputing"},{"key":"2633_CR9","first-page":"1531","volume":"5","author":"F Fleuret","year":"2004","unstructured":"Fleuret F. Fast binary feature selection with conditional mutual information. J Mach Learn Res, 2004, 5:1531\u20131555","journal-title":"J Mach Learn Res"},{"key":"2633_CR10","doi-asserted-by":"publisher","first-page":"210","DOI":"10.4218\/etrij.11.0110.0237","volume":"33","author":"H R Cheng","year":"2011","unstructured":"Cheng H R, Qin Z G, Feng C S, et al. Conditional mutual information-based feature selection analyzing for synergy and redundancy. ETRI J, 2011, 33:210\u2013218","journal-title":"ETRI J"},{"key":"2633_CR11","first-page":"687","volume-title":"Proceedings of the 12th International Conference on Neural Information Processing Systems","author":"H H Yang","year":"1999","unstructured":"Yang H H, Moody J. Data visualization and feature selection:new algorithms for nongaussian data. In: Proceedings of the 12th International Conference on Neural Information Processing Systems. Cambridge: MIT Press, 1999. 687\u2013693"},{"key":"2633_CR12","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","volume":"24","author":"J R Vergara","year":"2014","unstructured":"Vergara J R, Est\u00e9evez P A. A review of feature selection methods based on mutual information. Neural Comput Appl, 2014, 24:175\u2013186","journal-title":"Neural Comput Appl"},{"key":"2633_CR13","first-page":"27","volume":"13","author":"G Brown","year":"2012","unstructured":"Brown G, Pocock A, M-Zhao J, et al. Conditional likelihood maximisation:a unifying framework for information theoretic feature selection. Mach J Learn Res, 2012, 13, 27\u201366","journal-title":"Mach J Learn Res"},{"key":"2633_CR14","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","volume":"27","author":"H C Peng","year":"2005","unstructured":"Peng H C, Long F H, Ding C. Feature selection based on mutual information:criteria of max-dependency, maxrelevance, and min-redundancy. IEEE Trans Pattern Anal Machine Intell, 2005, 27:1226\u20131238","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"2633_CR15","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1142\/S0219720005001004","volume":"03","author":"C Ding","year":"2005","unstructured":"Ding C, Peng H. Minimum redundancy feature selection from microarray gene expression data. J Bioinform Comput Biol, 2005, 03:185\u2013205","journal-title":"J Bioinform Comput Biol"},{"key":"2633_CR16","doi-asserted-by":"publisher","first-page":"1526","DOI":"10.1158\/1078-0432.CCR-18-2013","volume":"25","author":"G Corredor","year":"2019","unstructured":"Corredor G, Wang X, Zhou Y, et al. Spatial architecture and arrangement of tumor-infiltrating lymphocytes for predicting likelihood of recurrence in early-stage non-small cell lung cancer. Clin Cancer Res, 2019, 25:1526\u20131534","journal-title":"Clin Cancer Res"},{"key":"2633_CR17","doi-asserted-by":"publisher","first-page":"63833","DOI":"10.1109\/ACCESS.2018.2876162","volume":"6","author":"A Toyoda","year":"2018","unstructured":"Toyoda A, Ogawa T, Haseyama M. Favorite video estimation based on multiview feature integration via KMvLFDA. IEEE Access, 2018, 6:63833\u201363842","journal-title":"IEEE Access"},{"key":"2633_CR18","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1080\/00949655.2015.1042378","volume":"86","author":"J R Berrendero","year":"2016","unstructured":"Berrendero J R, Cuevas A, Torrecilla J L. The mRMR variable selection method:a comparative study for functional data. J Stat Comput Simul, 2016, 86:891\u2013907","journal-title":"J Stat Comput Simul"},{"key":"2633_CR19","first-page":"1157","volume":"3","author":"I Guyon","year":"2003","unstructured":"Guyon I, Elisseeff A. An introduction to variable and feature selection. Mach J Learn Res, 2003, 3:1157\u20131182","journal-title":"Mach J Learn Res"},{"key":"2633_CR20","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"T R Golub","year":"1999","unstructured":"Golub T R, Slonim D K, Tamayo P, et al. Molecular classification of cancer:class discovery and class prediction by gene expression monitoring. Science, 1999, 286:531\u2013537","journal-title":"Science"},{"key":"2633_CR21","first-page":"4963","volume":"62","author":"G J G Gordon","year":"2002","unstructured":"Gordon G J G, Jensen R V R, Hsiao L-L L, et al. Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res, 2002, 62:4963\u20134967","journal-title":"Cancer Res"},{"key":"2633_CR22","doi-asserted-by":"publisher","first-page":"6745","DOI":"10.1073\/pnas.96.12.6745","volume":"96","author":"U Alon","year":"1999","unstructured":"Alon U, Barkai N, Notterman D A, et al. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci USA, 1999, 96:6745\u20136750","journal-title":"Proc Natl Acad Sci USA"},{"key":"2633_CR23","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1056\/NEJMoa030847","volume":"349","author":"E Tian","year":"2003","unstructured":"Tian E, Zhan F, Walker R, et al. The role of the Wnt-signaling antagonist DKK1 in the development of osteolytic lesions in multiple myeloma. New Engl J Med, 2003, 349:2483\u20132494","journal-title":"New Engl J Med"},{"key":"2633_CR24","doi-asserted-by":"publisher","first-page":"51","DOI":"10.2353\/jmoldx.2006.050079","volume":"8","author":"M E Burczynski","year":"2006","unstructured":"Burczynski M E, Peterson R L, Twine N C, et al. Molecular classification of crohn\u2019s disease and ulcerative colitis patients using transcriptional profiles in peripheral blood mononuclear cells. J Mol Diagn, 2006, 8:51\u201361","journal-title":"J Mol Diagn"},{"key":"2633_CR25","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/415436a","volume":"415","author":"S L Pomeroy","year":"2002","unstructured":"Pomeroy S L, Tamayo P, Gaasenbeek M, et al. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature, 2002, 415:436\u2013442","journal-title":"Nature"},{"key":"2633_CR26","doi-asserted-by":"crossref","first-page":"136","DOI":"10.5402\/2012\/712032","volume":"2012","author":"Y N Singh","year":"2012","unstructured":"Singh Y N, Singh S K, Ray A K. Bioelectrical signals as emerging biometrics:issues and challenges. ISRN Signal Process, 2012, 2012:136\u2013151","journal-title":"ISRN Signal Process"},{"key":"2633_CR27","doi-asserted-by":"publisher","first-page":"31","DOI":"10.2353\/jmoldx.2006.050056","volume":"8","author":"D Chowdary","year":"2006","unstructured":"Chowdary D, Lathrop J, Skelton J, et al. Prognostic gene expression signatures can be measured in tissues collected in RNAlater preservative. J Mol Diagn, 2006, 8:31\u201339","journal-title":"J Mol Diagn"},{"key":"2633_CR28","doi-asserted-by":"publisher","first-page":"2769","DOI":"10.1214\/009053607000000505","volume":"35","author":"G J Sz\u00e9ekely","year":"2007","unstructured":"Sz\u00e9ekely G J, Rizzo M L, Bakirov N K. Measuring and testing dependence by correlation of distances. Ann Statist, 2007, 35:2769\u20132794","journal-title":"Ann Statist"},{"key":"2633_CR29","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. Theoretical and empirical analysis of ReliefF and RReliefF. Mach Learn, 2003, 53:23\u201369","journal-title":"Mach Learn"}],"container-title":["Science China Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-019-2633-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11432-019-2633-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11432-019-2633-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,29]],"date-time":"2024-07-29T06:37:48Z","timestamp":1722235068000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11432-019-2633-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,1]]}},"alternative-id":["2633"],"URL":"https:\/\/doi.org\/10.1007\/s11432-019-2633-y","relation":{},"ISSN":["1674-733X","1869-1919"],"issn-type":[{"value":"1674-733X","type":"print"},{"value":"1869-1919","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]},"assertion":[{"value":"25 April 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 August 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"112103"}}