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A dynamic classifier ensemble selection approach for noise data

Dynamic classifier ensemble selection (DCES) plays a strategic role in the field of multiple classifier systems. The real data to be classified often include a large amount of noise, so it is important to study the noise-immunity ability of various DCES strategies. This paper introduces a group meth...

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Bibliographic Details
Published in:Information sciences 2010-09, Vol.180 (18), p.3402-3421
Main Authors: Xiao, Jin, He, Changzheng, Jiang, Xiaoyi, Liu, Dunhu
Format: Article
Language:English
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Summary:Dynamic classifier ensemble selection (DCES) plays a strategic role in the field of multiple classifier systems. The real data to be classified often include a large amount of noise, so it is important to study the noise-immunity ability of various DCES strategies. This paper introduces a group method of data handling (GMDH) to DCES, and proposes a novel dynamic classifier ensemble selection strategy GDES-AD. It considers both accuracy and diversity in the process of ensemble selection. We experimentally test GDES-AD and six other ensemble strategies over 30 UCI data sets in three cases: the data sets do not include artificial noise, include class noise, and include attribute noise. Statistical analysis results show that GDES-AD has stronger noise-immunity ability than other strategies. In addition, we find out that Random Subspace is more suitable for GDES-AD compared with Bagging. Further, the bias–variance decomposition experiments for the classification errors of various strategies show that the stronger noise-immunity ability of GDES-AD is mainly due to the fact that it can reduce the bias in classification error better.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2010.05.021