Non-competence reliability in multi-classification based on error-correcting output codes

被引：1

作者：

Lei L. ^{[1
]}

Wang X. ^{[1
]}

Quan W. ^{[2
]}

Luo X. ^{[3
]}

机构：

[1] Air and Missile Defense College, Air Force Engineering University, Xi'an

[2] Air Traffic Control and Navigation College, Air Force Engineering University, Xi'an

[3] Information and Navigation College, Air Force Engineering University, Xi'an

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 12期

关键词：

Classifier competence; Error-correcting output code (ECOC); Multi-classification; Support vector data description (SVDD);

D O I：

10.3969/j.issn.1001-506X.2017.12.01

中图分类号：

学科分类号：

摘要：

Error-correcting output code (ECOC) has been an established technique for multi-classification due to its simpleness and efficiency. However, the non-competent classifiers emerge when they classify an instance whose real class does not belong to one of the subclass sets which are used to learn the classifier. In this regard, in order to analyse the non-competence problem in the ECOC decomposing framework, a new weighted decoding strategy based on classifiers' competence ability is presented as the solution, which can strengthen the influence of competent classifiers and reduce that of non-competent ones on classification performance through learning weight coefficient of base classifiers. Meanwhile, the support vector data description is applied to compute the distance of instances to each class. The statistic simulations based on UCI datasets corroborate the proposed method. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：2637 / 2645

页数：8