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Proceedings of the 2nd International Symposium on Information Processing (ISIP 2009)

Huangshan, China, August 21-23, 2009

Editors: Fei Yu, Jian Shu, and Guangxue Yue

AP Catalog Number: AP-PROC-CS-09CN002

ISBN: 978-952-5726-02-2 (Print), 978-952-5726-03-9 (CD-ROM)

Page(s): 414-417

A High Performance Algorithm for Text Feature Automatic Selection

Jin Dai, Zhongshi He, and Feng Hu

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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.

Index Terms

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

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