Large Margin Distribution Machine for Imbalanced Data Classification

被引：0
|
作者：
Wang, DingXiang [1 ]
Zhang, XiaoGang [1 ]
Cheng, FanYong [2 ]
机构：
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China
[2] Anhui Polytech Univ, Coll Elect Engn, Wuhu 241000, Anhui, Peoples R China
来源：
2018 13TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2018年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The imbalanced data classification problem is common in many real-world supervised learning tasks, and always leads to the lower class-prediction accuracy of the minority class. In this paper, a modified large margin distribution machine is proposed by adjust the kernel matrix based on the distribution of the data near the class boundary, to improve the class-prediction accuracy. Through theoretical analysis backed by empirical study, it shows that the proposed algorithm in this paper works effectively on several UCI datasets.
引用
收藏
页码：893 / 898
页数：6
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