Least squares DAGSVM for multiclass classification

被引：0
作者：
Wu, Haoyu [1 ]
Zhou, Zhijian [1 ]
机构：
[1] College of Science, China Agricultural University, Beijing
来源：
Journal of Information and Computational Science | 2015年 / 12卷 / 18期
关键词：
DAGSVM; Least squares DAGSVM; Multiclass classification;
D O I：
10.12733/jics20150092
中图分类号：
学科分类号：
摘要：
The support vector machine was originally put forward as an effective learning machine for binary classification problems. How to extend it for multiclass problems is still a significant issue. Therefore, we propose a new algorithm for multiclass classification called Least Squares DAGSVM (LS-DAGSVM), which has less computing time, comparable accuracy and better performance compared to other algorithms. In this paper, the multiclass classification problem is divided into sequential binary classification problems. Then testing dataset is predicted in the decision functions. After that the result of testing dataset can be obtained from the algorithm. Experiment results based on eight benchmark datasets of UCI testify the feasibility and validity of the proposed algorithm. © 2015 by Binary Information Press.
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页码：6863 / 6871
页数：8
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