A High Performance Algorithm for Text Feature Automatic Selection

被引：0

作者：

Dai, Jin ^{[1
]}

He, Zhongshi ^{[1
]}

Hu, Feng ^{[2
]}

机构：

[1] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China

[2] Chongqing Univ Posts & Telecommun, Inst Comp Sci & Technol, Chongqing 400065, Peoples R China

来源：

ISIP: 2009 INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING, PROCEEDINGS | 2009年

关键词：

text classification; feature selection; cloud model; membership degree; dynamic clustering;

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：

Feature selection is an effective method for reducing the size of text feature space. So far, some effective methods for feature selection have been developed. For the purpose of acquiring the optimal number of features, these methods mainly depend on observation or experience. In this paper, by combining the overall with the local distribution of features in categories, a high performance algorithm for feature automation selection (Named FAS) is proposed. By using FAS, the feature set can be obtained automatically. Besides, it can effectively amend the distribution of features by using cloud model theory. Analysis and open experimental results show the selected feature set has fewer features and better classification performance than the existing methods.

引用

页码：414 / +

页数：2

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