Feature selection in text classification via SVM and LSI

被引：0

作者：

Wang, Ziqiang ^{[1
]}

Zhang, Dexian ^{[1
]}

机构：

[1] Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450052, Peoples R China

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2006, PT 1 | 2006年 / 3971卷

关键词：

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：

Text classification is a problem of assigning a document into one or more predefined classes. One of the most interesting issues in text categorization is feature selection. This paper proposes a novel approach in feature selection based on support vector machine(SVM) and latent semantic indexing(LSI), which can identify LSI-subspace that is suited for classification. Experimental results show that the proposed method can achieve higher classification accuracies and is of less training and prediction time.

引用

页码：1381 / 1386

页数：6

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