Boosting Naive Bayes Text Categorization by Using Cloud Model

被引：0
|
作者：
Wan, Jian [1 ]
He, Tingting [1 ]
Chen, Jinguang [2 ,3 ]
Dong, Jinling [1 ]
机构：
[1] Huazhong Normal Univ, Dept Comp Sci & Technol, Wuhan 430079, Peoples R China
[2] Huazhong Normal Univ, Engn & Res Ctr Informat Technol Educ, Wuhan 430079, Peoples R China
[3] Huzhou Teachers Coll, Sch Teacher Educ, Wuhan 313000, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON COMPUTER, ELECTRICAL, AND SYSTEMS SCIENCES, AND ENGINEERING (CESSE 2011) | 2011年
基金：
中国国家自然科学基金;
关键词：
Naive Bayes; Cloud Model; Feature Selection; Text Categorization; FEATURE-SELECTION;
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：
This paper presents a method which improves effectiveness of Naive Bayes text categorization by using cloud model. The traditional Naive Bayes text categorization directly uses term frequency to describe the relationship between words and categories. In deed, there are many words with high frequency do not have a close relevance with the category. To solve this problem, we introduce cloud model theory into Naive Bayes text classification and build a new feature selection system. By using numerical characteristics of cloud, we obtain more representative features. Experimental results on 20 Newsgroups show that our method can improve accuracy of text categorization remarkably.
引用
收藏
页码：165 / +
页数：2
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