• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学

• 人工智能与数据挖掘 • 上一篇    下一篇

  

  • 收稿日期:2018-06-19 修回日期:2018-09-16 出版日期:2019-04-25
  • 基金资助:

    国家自然科学基金(61773014)

An evaluation method for multiclass classification SVM
structure based on IG ratio of classification attributes

LI Jundi,ZHANG Zhengjun,ZHUANG Lichun,ZHANG Naijin   

  1. (School of Science,Nanjing University of Science and Technology,Nanjing 210094,China)
  • Received:2018-06-19 Revised:2018-09-16 Online:2019-04-25

Abstract:

The multiclass classification SVM based on the combination of binary tree structures has a small number of binary SVMs, and can avoid the occurrence of inseparable and repellent regions. Since the combination methods of multiclass classification SVM based on binary tree structure lack specific evaluation criteria for category combination, we propose a multiclass classification SVM structure evaluation method based on information gain (IG) ratio of classification attributes, define the IG ratio of classification attributes, and divide multiple classes into left category and right category. We calculate the IG ratio dependent on the classification attribute of variables for each possible combination of categories, and take the maximum value of the IG ratio as the evaluation criterion of this combination. Empirical analysis on Iris in the UCI database shows that the proposed method has a high recognition rate for multiclass classification SVM when the maximum value is taken as the evaluation criterion .
 

Key words: SVM, information gain ratio, classification attribute, binary tree, multiclass classification, SVM, information gain ratio, classification attribute