Computer Science

Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 89-94.doi: 10.11896/JsJkx.190500089

• Artificial Intelligence • Previous Articles     Next Articles

Multiclass Cost-sensitive Classification Based on Error Correcting Output Codes

WU Chong-ming1, WANG Xiao-dan2, XUE Ai-Jun2 and LAI Jie2   

  1. 1 Business School,XiJing University,Xi’an 710123,China
    2 College of Air and Missile Defense,Air force Engineering University,Xi’an 710051,China
  • Published:2020-07-07
  • About author:WU Chong-ming, born in 1966, Ph.D, associate professor.Hismain research interests include machine learning and intelligent information processing.
    WANG Xiao-dan, born in 1966, Ph.D, professor.Her research interests include machine learning, pattern recognition.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61876189,61273275,61703426).

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Abstract: Approach of multiclass cost-sensitive classification based on error correcting output codes is studied in this paper,and a new framework to decompose the complex multiclass cost-sensitive classification problem into a series of binary cost-sensitive classification problems is proposed.In order to obtain the binary cost matrix of each binary cost-sensitive base classifier,a method of computing the expected misclassification costs from the given multiclass cost matrix is proposed,and the general formula for computing the binary costs are given.Experimental results on artificial datasets and UCI datasets show that the proposed method has similar or even better performance in comparison with the existing methods.

Key words: Binary cost matrix, Error correcting output codes, Multiclass cost matrix, Multiclass cost-sensitive classification

CLC Number: 

  • TP391
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