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Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 524017, 6 pages
Research Article

A Heuristic Feature Selection Approach for Text Categorization by Using Chaos Optimization and Genetic Algorithm

1School of Information Science and Engineering, Hunan University, Changsha 410082, China
2School of Software, Hunan Vocational College of Science and Technology, Changsha 410118, China
3College of Electrical and Information Engineering, Hunan University, Changsha 410082, China

Received 10 October 2013; Revised 13 November 2013; Accepted 17 November 2013

Academic Editor: Gelan Yang

Copyright © 2013 Hao Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Due to the era of Big Data and the rapid growth in textual data, text classification becomes one of the key techniques for handling and organizing the text data. Feature selection is the most important step in automatic text categorization. In order to choose a subset of available features by eliminating unnecessary features to the classification task, a novel text categorization algorithm called chaos genetic feature selection optimization is proposed. The proposed algorithm selects the optimal subsets in both empirical and theoretical work in machine learning and presents a general framework for text categorization. Experimental results show that the proposed algorithm simplifies the feature selection process effectively and can obtain higher classification accuracy with a smaller feature set.