Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34242
Title: Dynamic fusion method using Localized Generalization Error Model
Authors: Chan, PPK
Yeung, DS
Ng, WWV
Lin, CM
Liu, JNK
Keywords: Dynamic fusion method
Localized Generalization Error Model (L-GEM)
Multiple Classifier Systems (MCSs)
Sensitivity
Issue Date: 2012
Publisher: Elsevier
Source: Information sciences, 2012, v. 217, p. 1-20 How to cite?
Journal: Information sciences 
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.
URI: http://hdl.handle.net/10397/34242
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2012.06.026
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