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Training Classifiers for Unbalanced Distribution and Cost-Sensitive Domains with ROC Analysis

  • Xiaolong Zhang
  • Chuan Jiang
  • Ming-jian Luo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4303)

Abstract

ROC (Receiver Operating Characteristic) has been used as a tool for the analysis and evaluation of two-class classifiers, even the training data embraces unbalanced class distribution and cost-sensitiveness. However, ROC has not been effectively extended to evaluate multi-class classifiers. In this paper, we proposed an effective way to deal with multi-class learning with ROC analysis. An EMAUC algorithm is implemented to transform a multi-class training set into several two-class training sets. Classification is carried out with these two-class training sets. Empirical results demonstrate that the classifiers trained with the proposed algorithm have competitive performance for unbalanced distribution and cost-sensitive domains.

Keywords

Classification ROC Cost-Sensitive Learning Error Correcting Output Coding 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xiaolong Zhang
    • 1
  • Chuan Jiang
    • 1
  • Ming-jian Luo
    • 1
  1. 1.School of Computer Science and TechnologyWuhan University of Science and TechnologyWuhanChina

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