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The improvement research of mutual information algorithm for text categorization

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成果类型:
期刊论文、会议论文
作者:
Kai, Lu*;Li, Chen
通讯作者:
Kai, Lu
作者机构:
[Li, Chen; Kai, Lu] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
通讯机构:
[Kai, Lu] C
Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
语种:
英文
关键词:
Composite ratio;Feature selection;Mutual information;Text categorization
期刊:
Advances in Intelligent Systems and Computing
ISSN:
2194-5357
年:
2014
卷:
278
页码:
225-232
会议名称:
8th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)
会议论文集名称:
Knowledge Engineering and Management
会议时间:
NOV 20-23, 2013
会议地点:
Shenzhen, PEOPLES R CHINA
会议主办单位:
[Kai, Lu;Li, Chen] Cent China Normal Univ, Sch Comp Sci, Wuhan, Peoples R China.
主编:
Zhenkun Wen<&wdkj&>Tianrui Li
出版地:
HEIDELBERGER PLATZ 3, D-14197 BERLIN, GERMANY
出版者:
Springer, Berlin, Heidelberg
ISBN:
978-3-642-54929-8
机构署名:
本校为第一且通讯机构
院系归属:
计算机学院
摘要:
Mutual information (MI) algorithm has many shortages in the feature selection of text categorization compared to other selection algorithms. For these shortages, this article introduces some important factors like term frequency or something that MI has not yet considered, and then puts forward the improved MI algorithm based on the composite ratio factor. And by the experiment the improved method can get a good improvement eff...

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