LI Kai , CHANG Sheng-ling , GAO Yue . Research of Ensemble Method of Classifiers Based on Clustering Technology[J]. Journal of Hebei University(Natural Science Edition), 2009 , 29(2) : 209 -213 . DOI: 10.3969/j.issn.1000-1565.2009.02.023

[1] OPITZ D, SHAVLIK J. Generating accurate and diverse members of a neural-network ensemble [M]. Cambridge, MA:The MIT Press 1996.
[2] ROSEN B E. Ensemble learning using decorrelated neural networks [J]. Connection Science 1996, 8(03).
[3] Zhi-Hua Zhou, Jianxin Wu, Wei Tang. Ensembling neural networks: Many could be better than all [J]. Artificial Intelligence: An International Journal 2002, 1/2(1/2).
[4] Altincay H. Decision trees using model ensemble-based nodes [J]. Pattern Recognition: The Journal of the Pattern Recognition Society 2007, 12(12).
[5] Ko AHR, Sabourin R, Britto AD, Oliveira L. Pairwise fusion matrix for combining classifiers [J]. Pattern Recognition: The Journal of the Pattern Recognition Society 2007, 8(8).
[6] SIRLANTZIS K, HOQUE S, FAIRHURST M C. Diversity in multiple classifier ensembles based on binary feature quantisation with application to face recognition [J]. Applied Soft Computing Journal 2008, 8(01).
[7] GIACINTO G, ROLI F. Design of effective neural network ensembles for image classification purposes [J]. Image and Vision Computing 2001, 19(9-10).
[8] Miin-Shen Yang, Kuo-Lung Wu. A similarity-based robust clustering method [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 2004, 4(4).
[9] YU Jian, HUANG Houkuan. A new weighting fuzzy C-means algorithms [A]. St Louis Missouri 2003.
[10] BLAKE C L, MERZ C J. UCI Repository of Machine Learning Database [EB/OL]. http://www.ics.uci.edu/～mlean/MLRepository.html 2008.