{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T15:50:36Z","timestamp":1777477836340,"version":"3.51.4"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2017,12,12]],"date-time":"2017-12-12T00:00:00Z","timestamp":1513036800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2018,9]]},"DOI":"10.1007\/s10489-017-1106-x","type":"journal-article","created":{"date-parts":[[2017,12,12]],"date-time":"2017-12-12T00:29:35Z","timestamp":1513038575000},"page":"2568-2579","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Maximum relevancy maximum complementary based ordered aggregation for ensemble pruning"],"prefix":"10.1007","volume":"48","author":[{"given":"Xin","family":"Xia","sequence":"first","affiliation":[]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,12,12]]},"reference":[{"key":"1106_CR1","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1016\/j.infsof.2014.07.005","volume":"58","author":"IH Laradji","year":"2015","unstructured":"Laradji IH, Alshayeb M, Ghouti L (2015) Software defect prediction using ensemble learning on selected features. Inf Softw Technol 58:388\u2013402","journal-title":"Inf Softw Technol"},{"issue":"3","key":"1106_CR2","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s10489-013-0440-x","volume":"39","author":"A Idris","year":"2013","unstructured":"Idris A, Khan A, Lee YS (2013) Intelligent churn prediction in telecom: employing mRMR feature selection and RotBoost based ensemble classification. Appl Intell 39(3):659\u2013672","journal-title":"Appl Intell"},{"issue":"2","key":"1106_CR3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1022859003006","volume":"51","author":"LI Kuncheva","year":"2003","unstructured":"Kuncheva LI, Whitaker CJ (2003) Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Mach Learn 51(2):181\u2013207","journal-title":"Mach Learn"},{"issue":"7\u20139","key":"1106_CR4","doi-asserted-by":"crossref","first-page":"1900","DOI":"10.1016\/j.neucom.2008.06.007","volume":"72","author":"I Partalas","year":"2009","unstructured":"Partalas I, Tsoumakas G, Vlahavas I (2009) Pruning an ensemble of classifiers via reinforcement learning. Neurocomputing 72(7\u20139):1900\u20131909","journal-title":"Neurocomputing"},{"key":"1106_CR5","doi-asserted-by":"crossref","unstructured":"Tamon C, Xiang J (2000) On the boosting pruning problem. In: European conference on machine learning","DOI":"10.1007\/3-540-45164-1_41"},{"issue":"3","key":"1106_CR6","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1109\/34.667881","volume":"20","author":"J Kittler","year":"1998","unstructured":"Kittler J, Hatef M, Duin RPW, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Mach Intell 20(3):226\u2013 239","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"1106_CR7","doi-asserted-by":"crossref","first-page":"3665","DOI":"10.1016\/j.patcog.2014.05.003","volume":"47","author":"AS Britto","year":"2014","unstructured":"Britto AS, Sabourin R, Oliveira LES (2014) Dynamic selection of classifiers\u2014a comprehensive review. Pattern Recogn 47(11):3665\u20133680","journal-title":"Pattern Recogn"},{"issue":"1","key":"1106_CR8","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1007\/s11063-011-9204-y","volume":"35","author":"MS Haghighi","year":"2012","unstructured":"Haghighi MS, Vahedian A, Yazdi HS (2012) Making diversity enhancement based on multiple classifier system by weight tuning. Neural Process Lett 35(1):61\u201380","journal-title":"Neural Process Lett"},{"issue":"2","key":"1106_CR9","first-page":"1835","volume":"12","author":"L Wang","year":"2011","unstructured":"Wang L, Sugiyama M, Jing Z, Yang C, Zhou ZH, Feng J (2011) A refined margin analysis for boosting algorithms via equilibrium margin. J Mach Learn Res 12(2):1835\u20131863","journal-title":"J Mach Learn Res"},{"key":"1106_CR10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2015.01.005","volume":"78","author":"B Sun","year":"2015","unstructured":"Sun B, Chen H, Wang J (2015) An empirical margin explanation for the effectiveness of DECORATE ensemble learning algorithm. Knowl-Based Syst 78:1\u201312","journal-title":"Knowl-Based Syst"},{"issue":"1","key":"1106_CR11","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10994-006-9449-2","volume":"65","author":"EK Tang","year":"2006","unstructured":"Tang EK, Suganthan PN, Yao X (2006) An analysis of diversity measures. Mach Learn 65(1):247\u2013271","journal-title":"Mach Learn"},{"issue":"8","key":"1106_CR12","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1016\/j.patcog.2007.01.031","volume":"40","author":"AHR Ko","year":"2007","unstructured":"Ko AHR, Sabourin R, Britto ADS Jr, Oliveira L (2007) Pairwise fusion matrix for combining classifiers. Pattern Recogn 40(8):2198\u20132210","journal-title":"Pattern Recogn"},{"key":"1106_CR13","first-page":"2131","volume":"6","author":"G Tsch","year":"2005","unstructured":"Tsch G, Warmuth MK (2005) Efficient margin maximizing with boosting. J Mach Learn Res 6:2131\u20132152","journal-title":"J Mach Learn Res"},{"issue":"4","key":"1106_CR14","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1109\/TNN.2010.2040484","volume":"21","author":"C Shen","year":"2010","unstructured":"Shen C, Li H (2010) Boosting through optimization of margin distributions. IEEE Trans Neural Netw 21 (4):659\u2013666","journal-title":"IEEE Trans Neural Netw"},{"issue":"4","key":"1106_CR15","doi-asserted-by":"crossref","first-page":"816","DOI":"10.1007\/s10489-015-0729-z","volume":"44","author":"Q Dai","year":"2016","unstructured":"Dai Q, Han XM (2016) An efficient ordering-based ensemble pruning algorithm via dynamic programming. Appl Intell 44(4):816\u2013830","journal-title":"Appl Intell"},{"key":"1106_CR16","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.patrec.2016.01.029","volume":"74","author":"GDC Cavalcanti","year":"2016","unstructured":"Cavalcanti GDC, Oliveira LS, Moura TJM, Carvalho GV (2016) Combining diversity measures for ensemble pruning. Pattern Recogn Lett 74:38\u201345","journal-title":"Pattern Recogn Lett"},{"issue":"134","key":"1106_CR17","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2013.07.054","volume":"134","author":"XC Yin","year":"2014","unstructured":"Yin XC, Huang K, Hao HW, Iqbal K, Wang ZB (2014) A novel classifier ensemble method with sparsity and diversity. Neurocomputing 134(134):214\u2013221","journal-title":"Neurocomputing"},{"key":"1106_CR18","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.inffus.2016.06.003","volume":"34","author":"H Ykhlef","year":"2017","unstructured":"Ykhlef H, Bouchaffra D (2017) An efficient ensemble pruning approach based on simple coalitional games. Information Fusion 34:28\u201342","journal-title":"Information Fusion"},{"key":"1106_CR19","unstructured":"Margineantu DD, Dietterich TG (1997) Pruning adaptive boosting. In: Proceedings of the fourteenth international conference on machine learning. Morgan Kaufmann Publishers Inc, pp 211\u2013 218"},{"issue":"3","key":"1106_CR20","first-page":"1315","volume":"7","author":"Y Zhang","year":"2006","unstructured":"Zhang Y, Burer S, Street WN (2006) Ensemble pruning via semi-definite programming. J Mach Learn Res 7(3):1315\u20131338","journal-title":"J Mach Learn Res"},{"issue":"139","key":"1106_CR21","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.neucom.2014.02.030","volume":"139","author":"H Zhang","year":"2014","unstructured":"Zhang H, Cao L (2014) A spectral clustering based ensemble pruning approach. Neurocomputing 139 (139):289\u2013297","journal-title":"Neurocomputing"},{"issue":"2","key":"1106_CR22","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/S0893-6080(02)00187-9","volume":"16","author":"B Bakker","year":"2003","unstructured":"Bakker B, Heskes T (2003) Clustering ensembles of neural network models. Neural Netw 16(2):261\u2013269","journal-title":"Neural Netw"},{"key":"1106_CR23","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2011.12.030","volume":"85","author":"Z Xie","year":"2012","unstructured":"Xie Z, Xu Y, Hu Q, Zhu P (2012) Margin distribution based bagging pruning. Neurocomputing 85:11\u201319","journal-title":"Neurocomputing"},{"issue":"3","key":"1106_CR24","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.neucom.2012.04.007","volume":"94","author":"F Yang","year":"2012","unstructured":"Yang F, Lu WH, Luo LK, Li T (2012) Margin optimization based pruning for random forest. Neurocomputing 94(3):54\u201363","journal-title":"Neurocomputing"},{"key":"1106_CR25","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/j.neucom.2012.07.026","volume":"99","author":"L Li","year":"2013","unstructured":"Li L, Zou B, Hu Q, Wu X, Yu D (2013) Dynamic classifier ensemble using classification confidence. Neurocomputing 99:581\u2013591","journal-title":"Neurocomputing"},{"issue":"6","key":"1106_CR26","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1016\/j.patrec.2013.01.003","volume":"34","author":"L Guo","year":"2013","unstructured":"Guo L, Boukir S (2013) Margin-based ordered aggregation for ensemble pruning. Pattern Recogn Lett 34 (6):603\u2013609","journal-title":"Pattern Recogn Lett"},{"key":"1106_CR27","unstructured":"Dai Q, Yao CS (2016) A hierarchical and parallel branch-and-bound ensemble selection algorithm. Appl Intell 1\u201317"},{"issue":"2","key":"1106_CR28","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1016\/j.knosys.2012.08.024","volume":"37","author":"Q Dai","year":"2013","unstructured":"Dai Q (2013) A competitive ensemble pruning approach based on cross-validation technique. Knowl-Based Syst 37(2):394\u2013414","journal-title":"Knowl-Based Syst"},{"issue":"2","key":"1106_CR29","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1007\/s10618-009-0138-1","volume":"19","author":"QL Zhao","year":"2009","unstructured":"Zhao QL, Jiang YH, Xu M (2009) A fast ensemble pruning algorithm based on pattern mining process. Data Min Knowl Disc 19(2):277\u2013292","journal-title":"Data Min Knowl Disc"},{"issue":"3","key":"1106_CR30","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.knosys.2013.10.024","volume":"56","author":"H Zhou","year":"2014","unstructured":"Zhou H, Zhao X, Wang X (2014) An effective ensemble pruning algorithm based on frequent patterns. Knowl-Based Syst 56(3):79\u201385","journal-title":"Knowl-Based Syst"},{"key":"1106_CR31","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neucom.2016.02.040","volume":"196","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B, Wo\u017aniak M (2016) Untrained weighted classifier combination with embedded ensemble pruning. Neurocomputing 196:14\u201322","journal-title":"Neurocomputing"},{"issue":"1","key":"1106_CR32","first-page":"1","volume":"101","author":"S \u00d6z\u00f6g\u00fcr-Aky\u00fcz","year":"2015","unstructured":"\u00d6z\u00f6g\u00fcr-Aky\u00fcz S, Windeatt T, Smith R (2015) Pruning of error correcting output codes by optimization of accuracy\u2014diversity trade off. Mach Learn 101(1):1\u201317","journal-title":"Mach Learn"},{"issue":"8","key":"1106_CR33","doi-asserted-by":"crossref","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"},{"issue":"1","key":"1106_CR34","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1016\/j.eswa.2014.07.052","volume":"42","author":"S Chernbumroong","year":"2015","unstructured":"Chernbumroong S, Shuang C, Yu H (2015) Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition. Expert Syst Appl 42(1):573\u2013583","journal-title":"Expert Syst Appl"},{"issue":"1","key":"1106_CR35","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1145\/584091.584093","volume":"5","author":"CEA Shannon","year":"2001","unstructured":"Shannon CEA (2001) A mathematical theory of communication. AT&T Tech J Acm Sigmobile Mobile Computing & Communications Review 5(1):3\u201355","journal-title":"AT&T Tech J Acm Sigmobile Mobile Computing & Communications Review"},{"issue":"1","key":"1106_CR36","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.inffus.2004.04.003","volume":"6","author":"A Tsymbal","year":"2005","unstructured":"Tsymbal A, Pechenizkiy M, Cunningham P (2005) Diversity in search strategies for ensemble feature selection. Information Fusion 6(1):83\u201398","journal-title":"Information Fusion"},{"key":"1106_CR37","unstructured":"Asuncion A, Newman D (2007) UCI machine learning repository [Online]. Available: \n                        http:\/\/www.ics.uci.edu\/mlearn\/MLRepository.html"},{"issue":"1","key":"1106_CR38","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor Newsl 11(1):10\u201318","journal-title":"SIGKDD Explor Newsl"},{"issue":"2","key":"1106_CR39","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1109\/TPAMI.2008.78","volume":"31","author":"G Martinez-Muoz","year":"2009","unstructured":"Martinez-Muoz G, Hernandez-Lobato D, Suarez A (2009) An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans Pattern Anal Mach Intell 31(2):245\u2013 59","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"1106_CR40","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","volume":"28","author":"JJ Rodriguez","year":"2006","unstructured":"Rodriguez JJ, Kuncheva LI, Alonso CJ (2006) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619\u201330","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"1106_CR41","first-page":"437","volume":"14","author":"I Mukherjee","year":"2011","unstructured":"Mukherjee I, Schapire RE (2011) A theory of multiclass boosting. J Mach Learn Res 14(1):437\u2013497","journal-title":"J Mach Learn Res"},{"issue":"2","key":"1106_CR42","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1214\/aoms\/1177704575","volume":"33","author":"JL Hodges","year":"1962","unstructured":"Hodges JL, Lehmann EL (1962) Rank methods for combination of independent experiments in analysis of variance. Ann Math Stat 33(2):482\u2013497","journal-title":"Ann Math Stat"},{"issue":"2","key":"1106_CR43","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(2):65\u201370","journal-title":"Scand J Stat"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10489-017-1106-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-017-1106-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-017-1106-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2018,8,14]],"date-time":"2018-08-14T00:25:10Z","timestamp":1534206310000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10489-017-1106-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,12]]},"references-count":43,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2018,9]]}},"alternative-id":["1106"],"URL":"https:\/\/doi.org\/10.1007\/s10489-017-1106-x","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,12]]}}}