Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2016, Article ID 1715780, 8 pages
http://dx.doi.org/10.1155/2016/1715780
Research Article

A Feature Selection Approach Based on Interclass and Intraclass Relative Contributions of Terms

School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, Shaanxi 710048, China

Received 29 March 2016; Revised 21 June 2016; Accepted 11 July 2016

Academic Editor: Elio Masciari

Copyright © 2016 Hongfang Zhou 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.

Linked References

  1. J. Yang, Y. Liu, X. Zhu, Z. Liu, and X. Zhang, “A new feature selection based on comprehensive measurement both in inter-category and intra-category for text categorization,” Information Processing and Management, vol. 48, no. 4, pp. 741–754, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. C. Shang, M. Li, S. Feng, Q. Jiang, and J. Fan, “Feature selection via maximizing global information gain for text classification,” Knowledge-Based Systems, vol. 54, pp. 298–309, 2013. View at Publisher · View at Google Scholar · View at Scopus
  3. A. K. Uysal and S. Gunal, “The impact of preprocessing on text classification,” Information Processing and Management, vol. 50, no. 1, pp. 104–112, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. B. Zhang, Analysis and Research on Feature Selection Algorithm for Text Classification, University of Science and Technology of China, Hefei, China, 2010.
  5. K. F. Yang, Y. K. Zhang, and Y. Li, “Feature selection method based on document frequency,” Computer Engineering, vol. 36, no. 17, pp. 33–38, 2010. View at Google Scholar
  6. H. Liu, Z. Yao, and Z. Su, “Optimization mutual information text feature selection method based on word frequency,” Computer Engineering, vol. 40, no. 7, pp. 179–182, 2014. View at Google Scholar
  7. H. Shi, D. Jia, and P. Miao, “Improved information gain text feature selection algorithm based on word frequency information,” Journal of Computer Applications, vol. 34, no. 11, pp. 3279–3282, 2014. View at Google Scholar
  8. S. Shan, S. Feng, and X. Li, “A comparative study on several typical feature selection methods for Chinese web page categorization,” Computer Engineering and Applications, vol. 39, no. 22, pp. 146–148, 2003. View at Google Scholar
  9. J. R. Quinlan, “Induction of decision trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986. View at Publisher · View at Google Scholar · View at Scopus
  10. Y. Yang and J. O. Pedersen, “A comparative study on feature selection in text categorization,” in Proceedings of the 14th International Conference on Machine Learning (ICML '97), pp. 412–420, Nashville, Tenn, USA, July 1997.
  11. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  12. A. K. Uysal and S. Gunal, “A novel probabilistic feature selection method for text classification,” Knowledge-Based Systems, vol. 36, no. 6, pp. 226–235, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. Xu, J.-T. Li, B. Wang, and C.-M. Sun, “Category resolve power-based feature selection method,” Journal of Software, vol. 19, no. 1, pp. 82–89, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Wang, H. Zhang, R. Liu, W. Lv, and D. Wang, “t-Test feature selection approach based on term frequency for text categorization,” Pattern Recognition Letters, vol. 45, no. 1, pp. 1–10, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. B. C. How and K. Narayanan, “An empirical study of feature selection for text categorization based on term weightage,” in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence (WI '04), pp. 599–602, IEEE Computer Society Press, Beijing, China, September 2004. View at Publisher · View at Google Scholar · View at Scopus
  16. S. S. Li and C. Q. Zong, “A new approach to feature selection for text categorization,” in Proceedings of the 2005 IEEE International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE '05), F. J. Ren and Y. X. Zhong, Eds., pp. 626–630, IEEE Press, Wuhan, China, November 2005. View at Publisher · View at Google Scholar · View at Scopus
  17. J. Yang, The Research of Text Representation and Feature Selection in Text Categorization, Jilin University, Changchun, China, 2013.
  18. G. Salton and C. Buckley, “Term-weighting approaches in automatic text retrieval,” Information Processing and Management, vol. 24, no. 5, pp. 513–523, 1988. View at Publisher · View at Google Scholar · View at Scopus
  19. H. Zhou, J. Guo, and Y. Wang, “A feature selection approach based on term distributions,” SpringerPlus, vol. 5, article 249, 2016. View at Publisher · View at Google Scholar
  20. F. Sebastiani, “Machine learning in automated text categorization,” ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002. View at Publisher · View at Google Scholar · View at Scopus