{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T09:15:24Z","timestamp":1762161324252},"reference-count":46,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2017,9,8]],"date-time":"2017-09-08T00:00:00Z","timestamp":1504828800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"name":"Science & Technology Department of Si chuan Province","award":["2013GZX0138"],"award-info":[{"award-number":["2013GZX0138"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2018,8]]},"DOI":"10.1007\/s11063-017-9703-6","type":"journal-article","created":{"date-parts":[[2017,9,11]],"date-time":"2017-09-11T16:52:09Z","timestamp":1505148729000},"page":"53-70","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Pruning the Ensemble of ANN Based on Decision Tree Induction"],"prefix":"10.1007","volume":"48","author":[{"given":"Sha","family":"Ding","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhi","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shi-yuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,9,8]]},"reference":[{"issue":"3","key":"9703_CR1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1007\/s10994-016-5576-6","volume":"105","author":"E Soltanmohammadi","year":"2016","unstructured":"Soltanmohammadi E, Naraghi-Pour M, van der Schaar M (2016) Context-based unsupervised ensemble learning and feature ranking. Mach Learn 105(3):459\u2013485","journal-title":"Mach Learn"},{"issue":"1","key":"9703_CR2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11063-011-9200-2","volume":"35","author":"M Termenon","year":"2012","unstructured":"Termenon M, Grana M (2012) A two stage sequential ensemble applied to the classification of Alzheimer\u2019s disease based on MRI features. Neural Process Lett 35(1):1\u201312","journal-title":"Neural Process Lett"},{"issue":"1","key":"9703_CR3","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1007\/s11063-014-9405-2","volume":"43","author":"SJ Lin","year":"2016","unstructured":"Lin SJ, Chen TF (2016) Multi-agent architecture for corporate operating performance assessment. Neural Process Lett 43(1):115\u2013132","journal-title":"Neural Process Lett"},{"key":"9703_CR4","first-page":"388","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 SoftwTechnol 58:388\u2013402","journal-title":"Inf SoftwTechnol"},{"issue":"2","key":"9703_CR5","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":"3","key":"9703_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\u2013239","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"11","key":"9703_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 Recognit 47(11):3665\u20133680","journal-title":"Pattern Recognit"},{"issue":"1","key":"9703_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":"1","key":"9703_CR9","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"issue":"1","key":"9703_CR10","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/j.inffus.2004.04.001","volume":"6","author":"P Melville","year":"2005","unstructured":"Melville P, Mooney RJ (2005) Creating diversity in ensembles using artificial data. Inf Fusion 6(1):99\u2013111","journal-title":"Inf Fusion"},{"issue":"2","key":"9703_CR11","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":"9703_CR12","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":"3","key":"9703_CR13","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1023\/A:1007662407062","volume":"37","author":"Y Freund","year":"1999","unstructured":"Freund Y, Schapire RE (1999) Large margin classification using the perceptron algorithm. Mach Learn 37(3):277\u2013296","journal-title":"Mach Learn"},{"issue":"4","key":"9703_CR14","doi-asserted-by":"crossref","first-page":"656","DOI":"10.1016\/j.neucom.2010.09.006","volume":"74","author":"Q Hu","year":"2011","unstructured":"Hu Q, Zhu P, Yang Y, Yu D (2011) Large-margin nearest neighbor classifiers via sample weight learning. Neurocomputing 74(4):656\u2013660","journal-title":"Neurocomputing"},{"key":"9703_CR15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.knosys.2014.06.005","volume":"67","author":"Q Hu","year":"2014","unstructured":"Hu Q, Li L, Wu X, Schaefer G, Yu D (2014) Exploiting diversity for optimizing margin distribution in ensemble learning. Knowl Based Syst 67:90\u2013104","journal-title":"Knowl Based Syst"},{"issue":"3","key":"9703_CR16","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":"9703_CR17","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\u2013218"},{"issue":"2","key":"9703_CR18","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\u2013259","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"9703_CR19","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Mu\u00f1oz G, Su\u00e1rez A (2006) Pruning in ordered bagging ensembles. In: Proceedings of the 23rd international conference on machine learning. ACM, pp 609\u2013616","DOI":"10.1145\/1143844.1143921"},{"issue":"6","key":"9703_CR20","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 Recognit Lett 34(6):603\u2013609","journal-title":"Pattern Recognit Lett"},{"issue":"4","key":"9703_CR21","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"},{"issue":"5","key":"9703_CR22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13042-014-0303-8","volume":"6","author":"M Bhardwaj","year":"2015","unstructured":"Bhardwaj M, Bhatnagar V (2015) Towards an optimally pruned classifier ensemble. Int J Mach Learn Cybern 6(5):1\u201320","journal-title":"Int J Mach Learn Cybern"},{"issue":"1\u20132","key":"9703_CR23","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"ZH Zhou","year":"2002","unstructured":"Zhou ZH, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137(1\u20132):239\u2013263","journal-title":"Artif Intell"},{"issue":"134","key":"9703_CR24","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"},{"issue":"3","key":"9703_CR25","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":"122","key":"9703_CR26","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.neucom.2013.06.026","volume":"122","author":"Q Dai","year":"2013","unstructured":"Dai Q (2013) A novel ensemble pruning algorithm based on randomized greedy selective strategy and ballot. Neurocomputing 122(122):258\u2013265","journal-title":"Neurocomputing"},{"issue":"2","key":"9703_CR27","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":"9703_CR28","doi-asserted-by":"crossref","unstructured":"Giacinto G, Roli F, Fumera G (2000) Design of effective multiple classifier systems by clustering of classifiers. In: Proceedings of 15th international conference on pattern recognition, vol 2, pp 160\u2013163","DOI":"10.1109\/ICPR.2000.906039"},{"issue":"139","key":"9703_CR29","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":"7\u20139","key":"9703_CR30","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":"9703_CR31","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. Inf Fusion 34:28\u201342","journal-title":"Inf Fusion"},{"issue":"2","key":"9703_CR32","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 Discov 19(2):277\u2013292","journal-title":"Data Min Knowl Discov"},{"key":"9703_CR33","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":"9703_CR34","first-page":"1","volume":"101","author":"Z r-Aky","year":"2015","unstructured":"r-Aky Z, Reyya 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":"4","key":"9703_CR35","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1162\/neco.1996.8.4.869","volume":"8","author":"D Partridge","year":"1996","unstructured":"Partridge D, Yates WB (1996) Engineering multiversion neural-net systems. Neural Comput 8(4):869\u2013893","journal-title":"Neural Comput"},{"issue":"2","key":"9703_CR36","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"key":"9703_CR37","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":"9703_CR38","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":"9","key":"9703_CR39","doi-asserted-by":"crossref","first-page":"2013","DOI":"10.1162\/089976600300015042","volume":"12","author":"V Vapnik","year":"2000","unstructured":"Vapnik V, Chapelle O (2000) Bounds on error expectation for support vector machines. Neural Comput 12(9):2013\u20132036","journal-title":"Neural Comput"},{"key":"9703_CR40","unstructured":"Bache K, Lichman M (2013) UCI machine learning repository"},{"issue":"1","key":"9703_CR41","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":"9703_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":"9703_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"},{"issue":"1","key":"9703_CR44","first-page":"1","volume":"1","author":"CJ Whitaker","year":"2002","unstructured":"Whitaker CJ, Kuncheva LI (2002) Examining the relationship between majority vote accuracy and diversity in bagging and boosting. Inform Softw Technol 1(1):1\u201319","journal-title":"Inform Softw Technol"},{"issue":"1","key":"9703_CR45","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":"1","key":"9703_CR46","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. Inf Fusion 6(1):83\u201398","journal-title":"Inf Fusion"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11063-017-9703-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9703-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-017-9703-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T09:37:43Z","timestamp":1570095463000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11063-017-9703-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,9,8]]},"references-count":46,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,8]]}},"alternative-id":["9703"],"URL":"https:\/\/doi.org\/10.1007\/s11063-017-9703-6","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,9,8]]}}}