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An Efficient Feature Selection Algorithm Based on Hybrid Clonal Selection Genetic Strategy for Text Categorization

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成果类型:
期刊论文
作者:
Jiang, Jiansheng;Shu, Wanneng;Jin, Huixia
通讯作者:
Jiang, J.(johnjjs@sina.com)
作者机构:
[Jiang, Jiansheng] Faculty of Mechanical and Electronic Engineering, China University of Petroleum Beijing, PRC. No.18, Fuxue Road, Changping Zone, Beijing 102249, China
[Jin, Huixia] Department of Physics and Telecom Engineering, Hunan City University, Yiyang 413008, China
[Shu, Wanneng] College of Computer Science, South-Central University for Nationalities, Wuhan 430074, China
语种:
英文
期刊:
Lecture Notes in Electrical Engineering
ISSN:
1876-1100
年:
2010
卷:
56
页码:
127-134
机构署名:
本校为其他机构
院系归属:
信息与电子工程学院
摘要:
Feature selection is commonly used to reduce dimensionality of datasets with thousands of features which would be impossible to process further. At present there are many methods to deal with text feature selection. To improve the performance of text categorization, we present a new feature selection algorithm for text categorization, called hybrid clonal selection genetic algorithm (HCSGA). Our experimental results, comparing HCSGA with an extensive and representative list of feature selection algorithms, show that HCSGA leads to a considerable increase in the classification accuracy, and is ...

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