{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T10:29:14Z","timestamp":1770978554618,"version":"3.50.1"},"publisher-location":"Cham","reference-count":93,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783319056951","type":"print"},{"value":"9783319056968","type":"electronic"}],"license":[{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2014,1,1]],"date-time":"2014-01-01T00:00:00Z","timestamp":1388534400000},"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":[],"published-print":{"date-parts":[[2014]]},"DOI":"10.1007\/978-3-319-05696-8_6","type":"book-chapter","created":{"date-parts":[[2014,5,9]],"date-time":"2014-05-09T14:19:39Z","timestamp":1399645179000},"page":"135-160","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Rotation-Based Ensemble Classifiers for High-Dimensional Data"],"prefix":"10.1007","author":[{"given":"Junshi","family":"Xia","sequence":"first","affiliation":[]},{"given":"Jocelyn","family":"Chanussot","sequence":"additional","affiliation":[]},{"given":"Peijun","family":"Du","sequence":"additional","affiliation":[]},{"given":"Xiyan","family":"He","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2014,3,26]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Achlioptas, D (2001) Database-friendly random projections. In: Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on principles of database systems, New York, NY, USA, 2001, pp 274\u2013281","DOI":"10.1145\/375551.375608"},{"issue":"4","key":"6_CR2","doi-asserted-by":"publisher","first-page":"608","DOI":"10.1016\/j.patcog.2005.08.017","volume":"39","author":"M Aksela","year":"2006","unstructured":"Aksela M, Laaksonen J (2006) Using diversity of errors for selecting members of a committee classifier. Pattern Recogn 39(4):608\u2013623","journal-title":"Pattern Recogn"},{"issue":"1\u20132","key":"6_CR3","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1109\/TGRS.2010.2051554","volume":"49","author":"KL Bakos","year":"2011","unstructured":"Bakos KL, Gamba P (2011) Hierarchical hybrid decision tree fusion of multiple hyperspectral data processing chains. IEEE Trans Geosci Remote Sens 49(1\u20132):388\u2013394","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-540-72523-7_50","volume":"4472","author":"JA Benediktsson","year":"2007","unstructured":"Benediktsson JA, Chanussot J, Fauvel M (2007) Multiple classifier systems in remote sensing: from basics to recent developments. Lect Notes Comput Sci 4472:501\u2013512","journal-title":"Lect Notes Comput Sci"},{"issue":"4","key":"6_CR5","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1109\/36.602526","volume":"35","author":"JA Benediktsson","year":"1997","unstructured":"Benediktsson JA, Sveinsson JR (1997) Hybrid consensus theoretic classification. IEEE Trans Geosci Remote Sens 35(4):833\u2013843","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"4","key":"6_CR6","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1109\/21.156582","volume":"22","author":"JA Benediktsson","year":"1992","unstructured":"Benediktsson JA, Swain PH (1992) Consensus theoretic classification methods. IEEE Trans Syst Man Cybern 22(4):688\u2013704","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"6_CR7","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1007\/s13042-010-0007-7","volume":"1","author":"B Biggio","year":"2010","unstructured":"Biggio B, Fumera G, Roli F (2010) Multiple classifier systems for robust classifier design in adversarial environments. J Mach Learn Cybern 1:27\u201341","journal-title":"J Mach Learn Cybern"},{"issue":"2","key":"6_CR8","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1109\/JSTARS.2012.2190266","volume":"5","author":"AC Braun","year":"2012","unstructured":"Braun AC, Weidner U, Hinz S (2012) Classification in high-dimensional feature spaces assessment using SVM, IVM and RVM with focus on simulated EnMap data. IEEE J Sel Top Appl Earth Observations Remote Sens 5(2):436\u2013443","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"issue":"2","key":"6_CR9","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"},{"issue":"1","key":"6_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forest. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"10","key":"6_CR11","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1109\/TGRS.2002.802476","volume":"40","author":"G Briem","year":"2002","unstructured":"Briem G, Benediktsson J, Sveinsson J (2002) Multiple classifiers applied to multisource remote sensing data. IEEE Trans Geosci Remote Sens 40(10):2291\u20132299","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"6_CR12","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.inffus.2004.04.004","volume":"6","author":"G Brown","year":"2005","unstructured":"Brown G, Wyatt J, Harris R, Yao X (2005) Diversity creation methods: a survey and categorisation. Inf Fusion 6(1):5\u201320","journal-title":"Inf Fusion"},{"issue":"6","key":"6_CR13","doi-asserted-by":"publisher","first-page":"1291","DOI":"10.1016\/S0031-3203(02)00121-8","volume":"36","author":"RK Bryll","year":"2003","unstructured":"Bryll RK, Gutierrez-Osuna R, Quek FKH (2003) Attribute bagging: improving accuracy of classifier ensembles by using random feature subsets. Pattern Recogn 36(6):1291\u20131302","journal-title":"Pattern Recogn"},{"issue":"6","key":"6_CR14","doi-asserted-by":"publisher","first-page":"2999","DOI":"10.1016\/j.rse.2008.02.011","volume":"112","author":"JC Chan","year":"2008","unstructured":"Chan JC, Paelinckx D (2008) Evaluation of Random Forest and AdBboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery. Remote Sens Environ 112(6):2999\u20133011","journal-title":"Remote Sens Environ"},{"issue":"7\u20139","key":"6_CR15","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1016\/j.neucom.2005.12.014","volume":"69","author":"A Chandra","year":"2006","unstructured":"Chandra A, Yao X (2006) Evolving hybrid ensembles of learning machines for better generalisation. Neurocomputing 69(7\u20139):686\u2013700","journal-title":"Neurocomputing"},{"key":"6_CR16","doi-asserted-by":"crossref","unstructured":"Cheriyadat A, Bruce LM (2003) Why principal component analysis is not an appropriate feature extraction method for hyperspectral data. In: Proceedings of IEEE geoscience and remote sensing symposium (IGARSS), Toulouse, France, 2003, pp 3420\u20133422","DOI":"10.1109\/IGARSS.2003.1294808"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Cunningham P, Carney J (2000) Diversity versus quality in classification ensembles based on feature selection. In: 11th European conference on machine learning, Barcelona, Spain, 2000, pp 109\u2013116","DOI":"10.1007\/3-540-45164-1_12"},{"issue":"4","key":"6_CR18","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1109\/LGRS.2004.837009","volume":"1","author":"F Dell\u2019Acqua","year":"2004","unstructured":"Dell\u2019Acqua F, Gamba P, Ferari A, Palmason BJA, Arnason K (2004) Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geosci Remote Sens Lett 1(4):322\u2013326","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"1","key":"6_CR19","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1613\/jair.105","volume":"2","author":"TG Dietterich","year":"1995","unstructured":"Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2(1):263\u2013286","journal-title":"J Artif Intell Res"},{"issue":"20","key":"6_CR20","doi-asserted-by":"publisher","first-page":"4609","DOI":"10.1080\/01431160701244872","volume":"28","author":"HTX Doan","year":"2007","unstructured":"Doan HTX, Foody GM (2007) Increasing soft classification accuracy through the use of an ensemble of classifiers. Int J Remote Sens 28(20):4609\u20134623","journal-title":"Int J Remote Sens"},{"issue":"4","key":"6_CR21","doi-asserted-by":"publisher","first-page":"4764","DOI":"10.3390\/s120404764","volume":"12","author":"P Du","year":"2012","unstructured":"Du P, Xia J, Zhang W, Tan K, Liu Y, Liu S (2012) Multiple classifier system for remote sensing image classification: a review. Sensors 12(4):4764\u20134792","journal-title":"Sensors"},{"issue":"3","key":"6_CR22","doi-asserted-by":"publisher","first-page":"031002","DOI":"10.3788\/COL201109.031002","volume":"9","author":"P Du","year":"2011","unstructured":"Du P, Zhang W, Xia J (2011) Hyperspectral remote sensing image classification based on decision level fusion. Chin Optics Lett 9(3):031002\u2013031004","journal-title":"Chin Optics Lett"},{"key":"6_CR23","doi-asserted-by":"publisher","first-page":"2857","DOI":"10.1109\/TGRS.2008.2000741","volume":"10","author":"R Duca","year":"2008","unstructured":"Duca R, Frate FD (2008) Hyperspectral and multiangle CHRIS-PROBA images for the generation of land cover maps. IEEE Trans Geosci Remote Sens 10:2857\u20132866","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2009\/783194","volume":"2","author":"M Fauvel","year":"2009","unstructured":"Fauvel M, Chanussot J, Benediktsson JA (2009) Kernel principal component analysis for the classification of hyperspectral remote sensing data over urban areas. EURASIP J Adv Signal Process 2:1\u201315","journal-title":"EURASIP J Adv Signal Process"},{"key":"6_CR25","unstructured":"Fleiss J (1981) Statistical methods for rates and proportions. Wiley, New York"},{"issue":"8","key":"6_CR26","doi-asserted-by":"publisher","first-page":"1658","DOI":"10.1016\/j.rse.2009.03.014","volume":"113","author":"GM Foody","year":"2009","unstructured":"Foody GM (2009) Classification accuracy comparison: hypothesis tests and the use of confidence intervals in evaluations of difference, equivalence and non-inferiority. Remote Sens Environ 113(8):1658\u20131663","journal-title":"Remote Sens Environ"},{"key":"6_CR27","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1080\/01431160600962566","volume":"28","author":"GM Foody","year":"2007","unstructured":"Foody GM, Boyd DS, Sanchez-Hernandez C (2007) Mapping a specific class with an ensemble of classifiers. Int J Remote Sens 28:1733\u20131746","journal-title":"Int J Remote Sens"},{"key":"6_CR28","unstructured":"Freund Y, Schapire RE (1996) Experiments with a new Boosting algorithm. In: International conference on machine learning, Bari, Italy, 1996, pp 148\u2013156"},{"issue":"1","key":"6_CR29","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1109\/TKDE.2011.206","volume":"25","author":"J Gao","year":"2013","unstructured":"Gao J, Liang F, Fan W, Sun Y, Han J (2013) A graph-based consensus maximization approach for combining multiple supervised and unsupervised models. IEEE Trans Knowl Data Eng 25(1):15\u201328","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"9\/10","key":"6_CR30","first-page":"697","volume":"19","author":"G Giacinto","year":"2001","unstructured":"Giacinto G, Roli F (2001) Design of effective neural network ensembles for image classification. Image Vis Comput J 19(9\/10):697\u2013705","journal-title":"Image Vis Comput J"},{"key":"6_CR31","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1049\/el:20000374","volume":"36","author":"G Giacinto","year":"2000","unstructured":"Giacinto G, Roli F, Fumera G (2000) Selection of image classifiers. Electron Lett 36:420\u2013422","journal-title":"Electron Lett"},{"key":"6_CR32","doi-asserted-by":"crossref","unstructured":"Giacinto G, Roli F, Vernazza G (1997) Comparison and combination of statistical and neural network algorithms for remote-sensing image classification. Neurocomputation in remote sensing data analysis. Springer, Berlin, pp 117\u2013124","DOI":"10.1007\/978-3-642-59041-2_13"},{"issue":"4","key":"6_CR33","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.patrec.2005.08.011","volume":"27","author":"PO Gislason","year":"2006","unstructured":"Gislason PO, Benediktsson JA, Sveinsson JR (2006) Random forests for land cover classification. Pattern Recogn Lett 27(4):294\u2013300","journal-title":"Pattern Recogn Lett"},{"issue":"4","key":"6_CR34","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.patrec.2005.08.004","volume":"27","author":"L Gmez-Chova","year":"2006","unstructured":"Gmez-Chova L, Fernndez-Prieto D, Calpe-Maravilla J, Soria-Olivas E, Vila-Francs J, Camps-Valls G (2006) Urban monitoring using multi-temporal SAR and multi-spectral data. Pattern Recogn Lett 27(4):234\u2013243","journal-title":"Pattern Recogn Lett"},{"key":"6_CR35","doi-asserted-by":"crossref","unstructured":"Guyon I, Gunn S, Nikravesh M, Zadeh L (2006) Feature extraction: foundations and applications. Springer, New York","DOI":"10.1007\/978-3-540-35488-8"},{"issue":"3","key":"6_CR36","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1109\/TGRS.2004.842481","volume":"43","author":"J Ham","year":"2005","unstructured":"Ham J, Chen Y, Crawford MM, Ghosh J (2005) Investigation of the random forest framework for classification of hyperspectral data. IEEE Trans Geosci Remote Sens 43(3):492\u2013501","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"6_CR37","doi-asserted-by":"publisher","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 Mach Intell 12(10):993\u20131001","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"2","key":"6_CR38","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.inffus.2004.03.001","volume":"6","author":"Z He","year":"2005","unstructured":"He Z, Xu X, Deng S (2005) A cluster ensemble method for clustering categorical data. Inf Fusion 6(2):143\u2013151","journal-title":"Inf Fusion"},{"issue":"8","key":"6_CR39","doi-asserted-by":"publisher","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 Mach Intell 20(8):832\u2013844","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"6_CR40","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/TIT.1968.1054102","volume":"14","author":"G Hughes","year":"1968","unstructured":"Hughes G (1968) On the mean accuracy of statistical pattern recognizers. IEEE Trans Inf Theory 14(1):55\u201363","journal-title":"IEEE Trans Inf Theory"},{"issue":"12","key":"6_CR41","doi-asserted-by":"publisher","first-page":"2396","DOI":"10.1109\/TPAMI.2011.84","volume":"33","author":"N Iam-On","year":"2011","unstructured":"Iam-On N, Boongoen T, Garrett S, Price C (2011) A link-based approach to the cluster ensemble problem. IEEE Trans Pattern Anal Mach Intell 33(12):2396\u20132409","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6_CR42","doi-asserted-by":"crossref","unstructured":"Richards JA, Jia X (2006) Remote sensing digital image analysis: an introduction. Springer, New York","DOI":"10.1007\/3-540-29711-1"},{"key":"6_CR43","unstructured":"Kang H-J, Lee S-W (2000) An information-theoretic strategy for constructing multiple classifier systems. In: International conference on pattern recognition (ICPR), Barcelona, Spain, 2000, pp 2483\u20132486"},{"issue":"11\u20132","key":"6_CR44","doi-asserted-by":"publisher","first-page":"3845","DOI":"10.1109\/TGRS.2007.903708","volume":"45","author":"K Kawaguchi","year":"2007","unstructured":"Kawaguchi K, Nishii R (2007) Hyperspectral image classification by bootstrap AdaBoost with random decision stumps. IEEE Trans Geosci Remote Sens 45(11\u20132):3845\u20133851","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6_CR45","unstructured":"Kohavi R, Wolpert DH (1996) Bias plus variance decomposition for zero-one loss functions. In: 13th international conference on machine learning (ICML), Bari, Italy, 1996, pp 275\u2013283"},{"key":"6_CR46","doi-asserted-by":"crossref","unstructured":"Kong Z, Cai Z (2007) Advances of research in fuzzy integral for classifiers\u2019 fusion. In: Proceedings of the eigth ACIS international conference on software engineering, artificial intelligence, networking, and parallel\/distributed computing, Washington, DC, USA pp 809\u2013814","DOI":"10.1109\/SNPD.2007.422"},{"key":"6_CR47","first-page":"231","volume":"7","author":"A Krogh","year":"1995","unstructured":"Krogh A, Vedelsby J (1995) Neural network ensembles, cross validation, and active learning. Adv Neural Inf Process Syst 7:231\u2013238","journal-title":"Adv Neural Inf Process Syst"},{"key":"6_CR48","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1007\/s100440200019","volume":"5","author":"S Kumar","year":"2002","unstructured":"Kumar S, Ghosh J, Crawford MM (2002) Hierarchical fusion of multiple classifiers for hyperspectral data analysis. Pattern Anal Appl 5:210\u2013220","journal-title":"Pattern Anal Appl"},{"key":"6_CR49","unstructured":"Kuncheva L, Whitaker C, Shipp C, Duin R (2000) Is independence good for combining classifiers? In: International conference on pattern recognition (ICPR), 2000, p 2168"},{"key":"6_CR50","doi-asserted-by":"crossref","unstructured":"Kuncheva LI (2004) Combining pattern classifiers: methods and algorithms. Wiley-Interscience, New Jersey","DOI":"10.1002\/0471660264"},{"issue":"1","key":"6_CR51","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.inffus.2004.04.009","volume":"6","author":"LI Kuncheva","year":"2005","unstructured":"Kuncheva LI (2005) Diversity in multiple classifier systems. Inf Fusion 6(1):3\u20134","journal-title":"Inf Fusion"},{"key":"6_CR52","doi-asserted-by":"crossref","unstructured":"Kuncheva LI, Rodriguez JJ (2007) An experimental study on rotation forest ensembles. In: Proceedings of the 7th international workshop on multiple classifier systems, Prague, Czech Republic, 2007, pp 459\u2013468","DOI":"10.1007\/978-3-540-72523-7_46"},{"issue":"2","key":"6_CR53","doi-asserted-by":"publisher","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"},{"key":"6_CR54","volume-title":"Signal theory methods in multispectral remote sensing","author":"DA Landgrebe","year":"1984","unstructured":"Landgrebe DA (1984) Signal theory methods in multispectral remote sensing. Wiley, New York"},{"issue":"3","key":"6_CR55","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1016\/j.rse.2004.01.007","volume":"90","author":"R Lawrence","year":"2004","unstructured":"Lawrence R, Bunna A, Powellb S, Zambon M (2004) Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Remote Sens Environ 90(3):331\u2013336","journal-title":"Remote Sens Environ"},{"issue":"5","key":"6_CR56","doi-asserted-by":"publisher","first-page":"823","DOI":"10.1080\/01431160600746456","volume":"28","author":"D Lu","year":"2007","unstructured":"Lu D, Weng Q (2007) A survey of image classification methods and techniques for improving classification performance. Int J Remote Sens 28(5):823\u2013870","journal-title":"Int J Remote Sens"},{"key":"6_CR57","unstructured":"Margineantu DD, Dietterich TG (1997) Pruning adaptive boosting. In: Proceedings of the fourteenth international conference on machine learning, San Francisco, CA, USA, 1997, pp 211\u2013218"},{"issue":"10","key":"6_CR58","doi-asserted-by":"publisher","first-page":"1483","DOI":"10.1016\/j.patcog.2005.02.020","volume":"38","author":"G Martinez-Munoz","year":"2005","unstructured":"Martinez-Munoz G, Suarez A (2005) Switching class labels to generate classification ensembles. Pattern Recogn 38(10):1483\u20131494","journal-title":"Pattern Recogn"},{"issue":"8","key":"6_CR59","doi-asserted-by":"publisher","first-page":"1778","DOI":"10.1109\/TGRS.2004.831865","volume":"42","author":"F Melgani","year":"2004","unstructured":"Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42(8):1778\u20131790","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"11","key":"6_CR60","first-page":"2539","volume":"39","author":"P Michail","year":"2002","unstructured":"Michail P, Benediktsson JA, Ioannis K (2002) The effect of classifier agreement on the accuracy of the combined classifier in decision level fusion. IEEE Trans Geosci Remote Sens 39(11):2539\u20132546","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"7","key":"6_CR61","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1109\/TGRS.2008.2010346","volume":"47","author":"B Mojaradi","year":"2009","unstructured":"Mojaradi B, Abrishami-Moghaddam H, Zoej M, Duin R (2009) Dimensionality reduction of hyperspectral data via spectral feature extraction. IEEE Trans Geosci Remote Sens 47(7):2091\u20132105","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"6_CR62","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.artmed.2007.07.003","volume":"41","author":"H Moon","year":"2007","unstructured":"Moon H, Ahn H, Kodell RL, Baek S, Lin C-J, Chen JJ (2007) Ensemble methods for classification of patients for personalized medicine with high-dimensional data. Artif Intell Med 41(3):197\u2013207","journal-title":"Artif Intell Med"},{"key":"6_CR63","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1007\/11815921_77","volume":"4109","author":"F Moreno-Seco","year":"2006","unstructured":"Moreno-Seco F, Inesta J, Ponce De Leon P, Mico L (2006) Comparison of classifier fusion methods for classification in pattern recognition tasks. Lect Notes Comput Sci 4109:705\u2013713","journal-title":"Lect Notes Comput Sci"},{"issue":"12","key":"6_CR64","first-page":"1747","volume":"93","author":"RR Navalgund","year":"2007","unstructured":"Navalgund RR, Jayaraman V, Roy PS (2007) Remote sensing applications: an overview. Current 93(12):1747\u20131766","journal-title":"Current"},{"issue":"2","key":"6_CR65","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.isprsjprs.2006.09.004","volume":"61","author":"H Nemmour","year":"2006","unstructured":"Nemmour H, Chibani Y (2006) Multiple support vector machines for land cover change detection: an application for mapping urban extensions. ISPRS J Photogrammetry Remote Sens 61(2):125\u2013133","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"issue":"3","key":"6_CR66","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1016\/j.cmpb.2011.03.018","volume":"104","author":"A Ozcift","year":"2011","unstructured":"Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Programs Biomed 104(3):443\u2013451","journal-title":"Comput Methods Programs Biomed"},{"issue":"5","key":"6_CR67","doi-asserted-by":"publisher","first-page":"2297","DOI":"10.1109\/TGRS.2009.2039484","volume":"48","author":"M Pal","year":"2010","unstructured":"Pal M, Foody GM (2010) Feature selection for classification of hyperspectral data by SVM. IEEE Trans Geosci Remote Sens 48(5):2297\u20132307","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"10","key":"6_CR68","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1016\/S0950-5849(97)00023-2","volume":"39","author":"D Partridge","year":"1997","unstructured":"Partridge D, Krzanowski W (1997) Software diversity: practical statistics for its measurement and exploitation. Inf Softw Technol 39(10):707\u2013717","journal-title":"Inf Softw Technol"},{"issue":"3","key":"6_CR69","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1109\/TGRS.2004.841417","volume":"43","author":"A Plaza","year":"2005","unstructured":"Plaza A, Martinez P, Plaza J, Perez R (2005) Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations. IEEE Trans Geosci Remote Sens 43(3):466\u2013479","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6_CR70","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1007\/s100440050038","volume":"2","author":"A Rahman","year":"1999","unstructured":"Rahman A, Fairhurst M (1999) Serial combination of multiple experts: a unified evaluation. Pattern Anal Appl 2:292\u2013311","journal-title":"Pattern Anal Appl"},{"issue":"1","key":"6_CR71","first-page":"1","volume":"3","author":"R Ranawana","year":"2006","unstructured":"Ranawana R, Palade V (2006) Multi-classifier systems: review and a roadmap for developers. Int J Hybrid Intell Syst 3(1):1\u201341","journal-title":"Int J Hybrid Intell Syst"},{"issue":"10","key":"6_CR72","doi-asserted-by":"publisher","first-page":"1619","DOI":"10.1109\/TPAMI.2006.211","volume":"28","author":"JJ Rodriguez","year":"2009","unstructured":"Rodriguez JJ, Kuncheva LI (2009) Rotation forest: a new classifier ensemble method. IEEE Trans Pattern Anal Mach Intell 28(10):1619\u20131630","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"6_CR73","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","volume":"67","author":"VF Rodriguez-Galianoa","year":"2011","unstructured":"Rodriguez-Galianoa VF, Ghimireb B, Roganb J, Chica-Olmoa M, Rigol-Sanchezc JP (2011) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogrammetry Remote Sens 67:93\u2013104","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"6_CR74","doi-asserted-by":"crossref","unstructured":"Rokach L (2010) Pattern classification using ensemble methods. World Scientific, Singapore","DOI":"10.1142\/7238"},{"issue":"3\u20134","key":"6_CR75","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.rse.2006.02.012","volume":"102","author":"F Ruitenbeek","year":"2006","unstructured":"Ruitenbeek F, Debba P, Meer F, Cudahy T, Meijde M, Hale M (2006) Mapping white micas and their absorption wavelengths using hyperspectral band ratios. Remote Sens Environ 102 (3\u20134):211\u2013222","journal-title":"Remote Sens Environ"},{"issue":"1","key":"6_CR76","first-page":"1","volume":"7","author":"D Ruta","year":"2000","unstructured":"Ruta D, Gabrys B (2000) An overview of classifier fusion methods. Comput Inf Syst 7(1):1\u201310","journal-title":"Comput Inf Syst"},{"issue":"7","key":"6_CR77","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the support of a high-dimensional distribution. Neural Comput 13(7):1443\u20131471","journal-title":"Neural Comput"},{"key":"6_CR78","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1109\/TGRS.2002.1006354","volume":"40","author":"PC Smits","year":"2002","unstructured":"Smits PC (2002) Multiple classifier systems for supervised remote sensing image classification based on dynamic classifier selection. IEEE Trans Geosci Remote Sens 40:801\u2013813","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"3","key":"6_CR79","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1016\/S0034-4257(00)00145-0","volume":"74","author":"BM Steele","year":"2000","unstructured":"Steele BM (2000) Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Remote Sens Environ 74(3):545\u2013556","journal-title":"Remote Sens Environ"},{"issue":"8","key":"6_CR80","first-page":"1021","volume":"27","author":"M Sugiyama","year":"2007","unstructured":"Sugiyama M (2007) Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. J Mach Learn Res 27(8):1021\u20131064","journal-title":"J Mach Learn Res"},{"issue":"1","key":"6_CR81","first-page":"117","volume":"8","author":"Q Sun","year":"2000","unstructured":"Sun Q, Ye XQ, Gu WK (2000) A new combination rules of evidence theory (in chinese). Acta Electronica Sinica 8(1):117\u2013119","journal-title":"Acta Electronica Sinica"},{"key":"6_CR82","doi-asserted-by":"crossref","unstructured":"Tsoumakas G, Partalas I, Vlahavas I (2009) An ensemble pruning primer. In: Okun O, Valentini G (eds) Applications of supervised and unsupervised ensemble methods. Springer, Berlin, pp 1\u201313","DOI":"10.1007\/978-3-642-03999-7_1"},{"key":"6_CR83","doi-asserted-by":"crossref","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, Berlin","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"6_CR84","doi-asserted-by":"publisher","first-page":"106","DOI":"10.5589\/m09-018","volume":"35","author":"B Waske","year":"2009","unstructured":"Waske B, Benediktsson JA, Arnason K, Sveinsson JR (2009) Mapping of hyperspectral aviris data using machine-learning algorithms. Can J Remote Sens 35:106\u2013116","journal-title":"Can J Remote Sens"},{"issue":"7","key":"6_CR85","doi-asserted-by":"publisher","first-page":"2880","DOI":"10.1109\/TGRS.2010.2041784","volume":"48","author":"B Waske","year":"2010","unstructured":"Waske B, Van Der Linden S, Benediktsson JA, Rabe A, Hostert P (2010) Sensitivity of support vector machines to random feature selection in classification of hyperspectral data. IEEE Trans Geosci Remote Sens 48(7):2880\u20132889","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"1","key":"6_CR86","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1109\/4235.585893","volume":"1","author":"DH Wolpert","year":"1997","unstructured":"Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67\u201382","journal-title":"IEEE Trans Evol Comput"},{"issue":"4","key":"6_CR87","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/34.588027","volume":"19","author":"K Woods","year":"1997","unstructured":"Woods K, Kegelmeyer WP, Bowyer K (1997) Combination of multiple classifiers using local accuracy estimates. IEEE Trans Pattern Anal Mach Intell 19(4):405\u2013410","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"1","key":"6_CR88","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.inffus.2013.04.006","volume":"16","author":"M Wozniak","year":"2014","unstructured":"Wozniak M, Grana M, Corchado E (2014) A survey of multiple classifier systems as hybrid systems. Inf Fusion 16(1):3\u201317","journal-title":"Inf Fusion"},{"key":"6_CR89","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1109\/LGRS.2013.2254108","volume":"11","author":"J Xia","year":"2014","unstructured":"Xia J, Du P, He X, Chanussot J (2014) Hyperspectral remote sensing image classification based on rotation forest. IEEE Geosci Remote Sens Lett 11:239\u2013243","journal-title":"IEEE Geosci Remote Sens Lett"},{"issue":"3","key":"6_CR90","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1109\/21.155943","volume":"22","author":"L Xu","year":"1992","unstructured":"Xu L, Krzyzak A, Suen CY (1992) Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Trans Syst Man Cybern 22(3):418\u2013435","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"7","key":"6_CR91","doi-asserted-by":"publisher","first-page":"2840","DOI":"10.1109\/TGRS.2010.2043533","volume":"48","author":"J-M Yang","year":"2010","unstructured":"Yang J-M, Kuo B-C, Yu P-T, Chuang C-H (2010) A dynamic subspace method for hyperspectral image classification. IEEE Trans Geosci Remote Sens 48(7):2840\u20132853","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"6_CR92","unstructured":"Zeng H, Triosell HJ (2004) Dimensionality reduction in hyperspectral image classification. In: IEEE International Conference on Image Processing (ICIP), Singapore, 2004, pp 913\u2013916"},{"issue":"2","key":"6_CR93","first-page":"173","volume":"64","author":"Y Zhang","year":"2010","unstructured":"Zhang Y (2010) Ten years of technology advancement in remote sensing and the research in the crc-agip lab in gce. Geomatica 64(2):173\u2013189","journal-title":"Geomatica"}],"container-title":["Advances in Computer Vision and Pattern Recognition","Fusion in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-05696-8_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,13]],"date-time":"2023-07-13T00:39:29Z","timestamp":1689208769000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-05696-8_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014]]},"ISBN":["9783319056951","9783319056968"],"references-count":93,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-05696-8_6","relation":{},"ISSN":["2191-6586","2191-6594"],"issn-type":[{"value":"2191-6586","type":"print"},{"value":"2191-6594","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014]]},"assertion":[{"value":"26 March 2014","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}