A SEMI-SUPERVISED LEARNING ALGORITHM BASED ON SVM FOR IMBALANCED DATA

被引：0

作者：

Du, Limin ^{[1
,2
]}

Xu, Yang ^{[1
]}

He, Xingxing ^{[1
]}

机构：

[1] Southwest Jiaotong Univ, Intelligent Control Dev Ctr, Chengdu 610031, Sichuan, Peoples R China

[2] Henan Univ, Pharm Coll, Kaifeng 475004, Henan, Peoples R China

来源：

UNCERTAINTY MODELLING IN KNOWLEDGE ENGINEERING AND DECISION MAKING | 2016年 / 10卷

基金：

美国国家科学基金会;

关键词：

CLASSIFIERS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In view of the semi-supervised classification problem for imbalanced data, a new semi-supervised learning algorithm based on SVM is proposed. In this method, the classification method based on SVM for unbalanced data is used to tag unlabeled documents in order to deal with the imbalance of data. The experimental results on several benchmark data sets show the validity of this method.

引用

页码：194 / 200

页数：7

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