A novel dynamic fusion method using localized generalization error model

Authors:
Daniel S. Yeung;Patrick P. K. Chan
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou, China;Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

A new dynamic classifier fusion method named L-GEM Fusion Method (LFM) for Multiple Classifier Systems (MCSs) is proposed. The localized generalization error upper bound for the neighborhood of a testing sample is calculated and used to estimate the local competence of base classifiers in MCSs. Different from the recent dynamic classifier selection methods, the proposed method consider not only the training error but also the sensitivity of the base classifier. Experimental results show that the MCSs using LFM has more accurate than other popular dynamic fusion methods.