A dynamic classifier ensemble selection approach for noise data

  • Authors:

  • Jin Xiao;Changzheng He;Xiaoyi Jiang;Dunhu Liu

  • Affiliations:

  • School of Business Administration, Sichuan University, Chengdu 610064, Sichuan Province, China;School of Business Administration, Sichuan University, Chengdu 610064, Sichuan Province, China;University of Münster, Department of Mathematics and Computer Science, Einsteinstraíe 62, 48149 Münster, Germany;Management Faculty, Chengdu University of Information Technology, Chengdu 610103, Sichuan Province, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

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.