期刊
INFORMATION SCIENCES
卷 180, 期 18, 页码 3402-3421出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.ins.2010.05.021
关键词
Multiple classifier systems; GMDH; Dynamic ensemble selection; Noise-immunity ability
资金
- Natural Science Foundation of China [70771067]
- Sino-Germany International Cooperation [70911130228]
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 GOES-AD is mainly due to the fact that it can reduce the bias in classification error better. (C) 2010 Elsevier Inc. All rights reserved.
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