{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T08:42:47Z","timestamp":1762504967726},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2004,10,28]],"date-time":"2004-10-28T00:00:00Z","timestamp":1098921600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pattern Anal Applic"],"published-print":{"date-parts":[[2004,12]]},"DOI":"10.1007\/s10044-004-0225-2","type":"journal-article","created":{"date-parts":[[2004,10,27]],"date-time":"2004-10-27T08:45:32Z","timestamp":1098866732000},"page":"285-295","source":"Crossref","is-referenced-by-count":11,"title":["Optimal resampling and classifier prototype selection in classifier ensembles using genetic algorithms"],"prefix":"10.1007","volume":"7","author":[{"given":"Hakan","family":"Alt\u0131n\u00e7ay","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2004,10,28]]},"reference":[{"key":"225_CR1","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s100440200011","volume":"5","author":"M Skurichina","year":"2002","unstructured":"Skurichina M, Duin RPW (2002) Bagging boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121\u2013135","journal-title":"Pattern Anal Appl"},{"key":"225_CR2","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24:123\u2013140","journal-title":"Mach Learn"},{"key":"225_CR3","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1023\/A:1007515423169","volume":"36","author":"E Bauer","year":"1999","unstructured":"Bauer E, Kohavi R (1999) An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn 36:105\u2013142","journal-title":"Mach Learn"},{"key":"225_CR4","doi-asserted-by":"crossref","unstructured":"Schapire RE (2002) The boosting approach to machine learning: an overview. In: Proceedings of the MSRI workshop on nonlinear estimation and classification, Berkeley, California","DOI":"10.1007\/978-0-387-21579-2_9"},{"key":"225_CR5","unstructured":"Collins M, Schapire RE, Singer Y (2000) Logistic regression, AdaBoost and Bregman distances. In: Proceedings of the 13th annual conference on computational learning theory, Palo Alto, California, June\/July 2000, pp 158\u2013169"},{"key":"225_CR6","unstructured":"Whitaker CJ, Kuncheva LI (2003) Examining the relationship between majority vote accuracy and diversity in bagging and boosting. Technical report, School of Informatics, University of Wales, Bangor, UK"},{"key":"225_CR7","unstructured":"Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. In: Tesauro G, Touretzky D, Leen T (eds) Advances in neural information processing systems, vol 7. MIT Press, Cambridge, Massachusetts, pp 231\u2013238"},{"key":"225_CR8","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1613\/jair.614","volume":"11","author":"D Opitz","year":"1999","unstructured":"Opitz D, Maclin R (1999) Popular ensemble methods: an empirical study. J Artif Intell Res 11:169\u2013198","journal-title":"J Artif Intell Res"},{"key":"225_CR9","doi-asserted-by":"crossref","unstructured":"Zenobi G, Cunningham P (2001) Using diversity in preparing ensembles of classifiers based on different feature subsets to minimize generalization error. Lecture notes in computer science, Springer, Berlin Heidelberg New York, p 2167","DOI":"10.1007\/3-540-44795-4_49"},{"key":"225_CR10","unstructured":"Melville P, Mooney RJ (2003) Constructing diverse classifier ensembles using artificial training examples. In: Proceedings of the 18th international joint conference on artificial intelligence (IJCAI 2003), Acapulco, Mexico, August 2003, pp 505\u2013510"},{"issue":"11","key":"225_CR11","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1016\/S0031-3203(01)00227-8","volume":"35","author":"M Demirekler","year":"2002","unstructured":"Demirekler M, Alt\u0131n\u00e7ay H (2002) Plurality voting based multiple classifier systems: statistically independent with respect to dependent classifier sets. Pattern Recogn 35(11):2365\u20132379","journal-title":"Pattern Recogn"},{"issue":"9\u201310","key":"225_CR12","first-page":"669","volume":"19","author":"G Giacinto","year":"2001","unstructured":"Giacinto G, Roli F (2001) Design of effective neural network ensembles for image classification purposes. Image Vision Comput 19(9\u201310):669\u2013707","journal-title":"Image Vision Comput"},{"key":"225_CR13","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 51:181\u2013207","journal-title":"Machine Learning"},{"key":"225_CR14","unstructured":"Ruta D, Gabrys B (2001) Analysis of the correlation between majority voting error and the diversity measures in multiple classifier systems. In: Proceedings of the 4th international ICSC symposium on soft computing and intelligent systems for industry, Paisley, Scotland, June 2001"},{"key":"225_CR15","unstructured":"Ruta D, Gabrys B (2004) Classifier selection for majority voting. More information at http:\/\/cis.paisley.ac.uk\/ruta-ci0\/publications.htm. Inform Fusion J (to appear)"},{"issue":"3\/4","key":"225_CR16","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1080\/095400996116802","volume":"8","author":"DW Opitz","year":"1996","unstructured":"Opitz DW, Shavlik JW (1996) Actively searching for an effective neural-network ensemble. Connect Sci 8(3\/4):337\u2013353","journal-title":"Connect Sci"},{"issue":"2","key":"225_CR17","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","volume":"28","author":"J Friedman","year":"2000","unstructured":"Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337\u2013374","journal-title":"Ann Stat"},{"issue":"10","key":"225_CR18","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1109\/34.58871","volume":"12","author":"LK Hansen","year":"1990","unstructured":"Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal Machine Intell 12(10):993\u20131001","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"issue":"4","key":"225_CR19","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1016\/S0893-6080(96)00098-6","volume":"10","author":"S Hashem","year":"1997","unstructured":"Hashem S (1997) Optimal linear combinations of neural networks. Neural Netw 10(4):599\u2013614","journal-title":"Neural Netw"},{"key":"225_CR20","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm. In: Proceedings of the 13th international conference on machine learning (ML\u201996), Bari, Italy, July 1996. Morgan Kauffmann, pp 148\u2013156"},{"issue":"8","key":"225_CR21","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/34.709601","volume":"20","author":"TK Ho","year":"1998","unstructured":"Ho TK (1998) The random subspace method for constructing decision forests. IEEE Trans Pattern Anal Machine Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"225_CR22","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","volume":"4","author":"D Whitley","year":"1994","unstructured":"Whitley D (1994) A genetic algorithm tutorial. Stat Comput 4:65\u201385","journal-title":"Stat Comput"},{"key":"225_CR23","volume-title":"Genetic algorithms and engineering design","author":"M Gen","year":"1997","unstructured":"Gen M, Cheng R (1997) Genetic algorithms and engineering design. Wiley, New York"},{"issue":"4","key":"225_CR24","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1109\/4235.887233","volume":"4","author":"LI Kuncheva","year":"2000","unstructured":"Kuncheva LI, Jain LC (2000) Designing classifier fusion systems by genetic algorithms. IEEE Trans Evol Comput 4(4):327\u2013336","journal-title":"IEEE Trans Evol Comput"},{"key":"225_CR25","doi-asserted-by":"crossref","unstructured":"Sirlantzis K, Fairhurst MC, Hoque S (2001) Genetic algorithms for multi-classifier system configuration: a case study in character recognition. In: Kittler J, Roli F (eds) Proceedings of the 2nd international workshop on multiple classifier systems (MCS 2001), Cambridge, UK, July 2001. Lecture notes in computer science. Springer, Berlin Heidelberg New York, pp 99\u2013108","DOI":"10.1007\/3-540-48219-9_10"},{"key":"225_CR26","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1016\/0167-8655(95)00050-Q","volume":"16","author":"L Lam","year":"1995","unstructured":"Lam L, Suen CY (1995) Optimal combinations of pattern classifiers. Pattern Recogn Lett 16:945\u2013954","journal-title":"Pattern Recogn Lett"},{"key":"225_CR27","unstructured":"Ruta D, Gabrys B (2001) Genetic algorithms for multi-classifier system configuration: a case study in character recognition. In: Kittler J, Roli F (eds) Proceedings of the 2nd international workshop on multiple classifier systems (MCS 2001), Cambridge, UK, July 2001. Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 399\u2013408"},{"key":"225_CR28","first-page":"79","volume-title":"Combining artificial neural nets","author":"D Opitz","year":"1999","unstructured":"Opitz D, Shavlik J (1999) A genetic algorithm approach for creating neural network ensembles. In: Sharkey AJC (ed) Combining artificial neural nets. Springer, Berlin Heidelberg New York, pp 79\u201399"},{"key":"225_CR29","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1016\/S0950-7051(99)00023-4","volume":"12","author":"S Thompson","year":"1999","unstructured":"Thompson S (1999) Pruning boosted classifiers with a real valued genetic algorithm. Knowl-Based Syst 12:277\u2013284","journal-title":"Knowl-Based Syst"},{"key":"225_CR30","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1016\/S0004-3702(02)00190-X","volume":"137","author":"Z Zhou","year":"2002","unstructured":"Zhou Z, Wu J, Tang W (2002) Ensembling neural networks: many could be better than all. Artif Intell 137:239\u2013263","journal-title":"Artif Intell"},{"key":"225_CR31","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1002\/cem.1180080107","volume":"8","author":"R Leardi","year":"1994","unstructured":"Leardi R (1994) Application of a genetic algorithm to feature selection under full validation conditions and to outlier detection. J Chemometr 8:65\u201379","journal-title":"J Chemometr"},{"key":"225_CR32","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1016\/0167-8655(89)90037-8","volume":"10","author":"W Siedlecki","year":"1989","unstructured":"Siedlecki W, Sklansky J (1989) A note on genetic algorithms for large-scale feature selection. Pattern Recogn Lett 10:335\u2013347","journal-title":"Pattern Recogn Lett"},{"issue":"3","key":"225_CR33","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 R, Matas J (1998) On combining classifiers. IEEE Trans Pattern Anal Machine Intell 20(3):226\u2013239","journal-title":"IEEE Trans Pattern Anal Machine Intell"},{"key":"225_CR34","unstructured":"Duin RPW, Tax DMJ (2000) Experiments with classifier combining rules. In: Kittler J, Roli F (eds) Proceedings of the 1st international workshop on multiple classifier systems (MCS 2000), Sardinia, Italy, June 2000. Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 16\u201329"},{"key":"225_CR35","doi-asserted-by":"crossref","unstructured":"Gunter S, Bunke H (2002) Generating classifier ensembles from multiple prototypes and its application to handwriting recognition. In: Proceedings of the 3rd international workshop on multiple classifier systems (MCS2002), Cagliari, Italy, June 2002. Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 179\u2013188","DOI":"10.1007\/3-540-45428-4_18"},{"key":"225_CR36","unstructured":"Duin RPW (2000) PRTools version 3.0: a Matlab toolbox for pattern recognition. Pattern Recognition Group, Delft University, The Netherlands"},{"key":"225_CR37","doi-asserted-by":"crossref","unstructured":"Roli F, Giacinto G, Vernazza G (2001) Methods for designing multiple classifier systems. In: Kittler J, Roli F (eds) Proceedings of the 2nd international workshop on multiple classifier systems (MCS 2001), Cambridge, UK, July 2001. Lecture notes in computer science, Springer, Berlin Heidelberg New York, pp 78\u201387","DOI":"10.1007\/3-540-48219-9_8"},{"issue":"3","key":"225_CR38","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1162\/evco.1999.7.3.205","volume":"7","author":"Deb Kalyanmoy","year":"1999","unstructured":"Kalyanmoy Deb (1999) Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evol Comput 7(3):205\u2013230","journal-title":"Evol Comput"}],"container-title":["Pattern Analysis and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-004-0225-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10044-004-0225-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10044-004-0225-2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,4,3]],"date-time":"2020-04-03T13:54:52Z","timestamp":1585922092000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10044-004-0225-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2004,10,28]]},"references-count":38,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2004,12]]}},"alternative-id":["225"],"URL":"https:\/\/doi.org\/10.1007\/s10044-004-0225-2","relation":{},"ISSN":["1433-7541","1433-755X"],"issn-type":[{"value":"1433-7541","type":"print"},{"value":"1433-755X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2004,10,28]]}}}