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计算机工程与科学

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

（School of Science,Nanjing University of Science and Technology,Nanjing 210094,China）

Received:2018-06-19 Revised:2018-09-16 Online:2019-04-25

摘要/Abstract

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

李君娣，张正军，庄立纯，张乃今. null[J]. 计算机工程与科学.

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

An evaluation method for multiclass classification SVM

structure based on IG ratio of classification attributes

[J]. Computer Engineering & Science.

An evaluation method for multiclass classification SVM

structure based on IG ratio of classification attributes

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