Cost-Sensitive Support Vector Machine for Semi-Supervised Learning

被引：25

作者：

Qi, Zhiquan ^{[1
]}

Tian, Yingjie ^{[1
]}

Shi, Yong ^{[1
]}

Yu, Xiaodan ^{[2
]}

机构：

[1] Chinese Acad Sci, Res Ctr Fictitious Econ & Data Sci, Beijing 100190, Peoples R China

[2] Univ Nebraska, Coll Informat Sci & Technol, Omaha, NE 68182 USA

来源：

2013 INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE | 2013年 / 18卷

基金：

中国国家自然科学基金;

关键词：

SVM; Cost-Sensitive; Semi-Supervised Learning; REGULARIZATION;

D O I：

10.1016/j.procs.2013.05.336

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Cost-sensitive learning has been a hot research topic in machine learning. Many cost-sensitive methods have been successfully applied in many real-world applications such as disease diagnosis, fraud detection and business decision making. In this paper, we proposed a new Cost-Sensitive Laplacian Support Vector Machine(called Cos-LapSVM), which can deal with the cost-sensitive problem in Semi-Supervised Learning. The effectiveness of the proposed method is demonstrated via experiments on UCI datasets.

引用

页码：1684 / 1689

页数：6

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