Dynamic fusion method using Localized Generalization Error Model

Authors:
Patrick P. K. Chan;Daniel S. Yeung;Wing W. Y. Ng;Chih Min Lin;James N. K. Liu
Affiliations:
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China;School of Computer Science and Engineering, South China University of Technology, Guangzhou 510006, China;Department of Electrical Engineering, Yuan Ze University, 135 Yuan-Tung Road, Chung Li 320, Taiwan, ROC;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
Venue:
Information Sciences: an International Journal
Year:
2012

Citing 31
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Abstract

Multiple Classifier Systems (MCSs), which combine the outputs of a set of base classifiers, were proposed as a method to develop a more accurate classification system. One fundamental issue is how to combine the base classifiers. In this paper, a new dynamic fusion method named Localized Generalization Error Model Fusion Method (LFM) for MCSs is proposed. The Localized Generalization Error Model (L-GEM) has been used to estimate the local competence of base classifiers in MCSs. L-GEM provides a generalization error bound for unseen samples located within neighborhoods of testing samples. Base classifiers with lower generalization error bounds are assigned higher weights. In contrast to the current dynamic fusion methods, LFM estimates the local competence of base classifiers not only using the information of training error but also the sensitivity of classifier outputs. The additional effect of the sensitivity on the performance of model and the time complexity of the LFM are discussed and analyzed. Experimental results show that the MCSs using the LFM as a combination method outperform those using the other 21 dynamic fusion methods in terms of testing accuracy and time.