Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 784392, 9 pages
http://dx.doi.org/10.1155/2014/784392
Research Article

Approach for Text Classification Based on the Similarity Measurement between Normal Cloud Models

College of Software Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

Received 16 October 2013; Accepted 8 January 2014; Published 23 February 2014

Academic Editors: R. Valencia-García and Y.-B. Yuan

Copyright © 2014 Jin Dai and Xin Liu. 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. 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
  2. S. K. Murthy, “Automatic construction of decision trees from data: a multi-disciplinary survey,” Data Mining and Knowledge Discovery, vol. 2, no. 4, pp. 345–389, 1998. View at Google Scholar · View at Scopus
  3. F. De Comité, R. Gilleron, and M. Tommasi, “Learning multi-label alternating decision trees from texts and data,” in Machine Learning and Data Mining in Pattern Recognition, pp. 35–49, Springer, Berlin, Germany, 2003. View at Google Scholar
  4. B. Yu, Z.-B. Xu, and C.-H. Li, “Latent semantic analysis for text categorization using neural network,” Knowledge-Based Systems, vol. 21, no. 8, pp. 900–904, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. M. E. Ruiz and P. Srinivasan, “Hierarchical text categorization using neural networks,” Information Retrieval, vol. 5, no. 1, pp. 87–118, 2002. View at Google Scholar · View at Scopus
  6. A. McCallum and K. A. Nigam, “comparison of event models for naive bayes text classification,” in Workshop on Learning for Text Categorization (AAAI '98), vol. 752, pp. 41–48, 1998.
  7. A. Z. Broder, M. Fontoura, E. Gabrilovich, A. Joshi, V. Josifovski, and T. Zhang, “Robust classification of rare queries using web knowledge,” in Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '07), pp. 231–238, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. L. H. Lee, D. Isa, W. O. Choo, and W. Y. Chue, “High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic,” Expert Systems with Applications, vol. 39, no. 1, pp. 1147–1155, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. T. Joachims, Text Categorization with Support Vector Machines: Learning with Many Relevant Features, Springer, Berlin, Germany, 1998.
  10. T.-Y. Wang and H.-M. Chiang, “One-against-one fuzzy support vector machine classifier: an approach to text categorization,” Expert Systems with Applications, vol. 36, no. 6, pp. 10030–10034, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. M. A. Kumar and M. Gopal, “A comparison study on multiple binary-class SVM methods for unilabel text categorization,” Pattern Recognition Letters, vol. 31, no. 11, pp. 1437–1444, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. A. Sun, E.-P. Lim, and Y. Liu, “On strategies for imbalanced text classification using SVM: a comparative study,” Decision Support Systems, vol. 48, no. 1, pp. 191–201, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. Y. -Yang and X. Liu, “A re-examination of text categorization methods,” in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 42–49, ACM, 1999.
  14. E. H. S. Han, G. Karypis, and V. Kumar, Text Categorization Using Weight Adjusted K-Nearest Neighbor Classification, Springer, Berlin, Germany, 2001.
  15. S. Jiang, G. Pang, M. Wu, and L. Kuang, “An improved K-nearest-neighbor algorithm for text categorization,” Expert Systems with Applications, vol. 39, no. 1, pp. 1503–1509, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. J.-S. Su, B.-F. Zhang, and X. Xu, “Advances in machine learning based text categorization,” Journal of Software, vol. 17, no. 9, pp. 1848–1859, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. L. H. Lee, D. Isa, W. O. Choo, and W. Y. Chue, “High Relevance Keyword Extraction facility for Bayesian text classification on different domains of varying characteristic,” Expert Systems with Applications, vol. 39, no. 1, pp. 1147–1155, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Y. Li, Artificial Intelligence with Uncertainty, National Defense Industry Press, Beijing, China, 2005.
  19. D. Y. Li and C. Y. Liu, “Study on the universality of the normal cloud model,” Engineering Science, vol. 6, no. 8, pp. 28–34, 2004. View at Google Scholar
  20. D.-Y. Li, C.-Y. Liu, Y. Du, and X. Han, “Artificial intelligence with uncertainty,” Journal of Software, vol. 15, no. 11, pp. 1583–1594, 2004. View at Google Scholar · View at Scopus
  21. P. Soucy and G. W. Mineau, “Feature selection strategies for text categorization,” in Proceedings of the 16th Conferenceof the Canadian Society for Computational Studies of Intelligence (CSCSI '03), Y. Xiang and B. Chaib-Draa, Eds., pp. 505–509, Springer, Halifax, UK, 2003.
  22. G.-W. Zhang, D.-Y. Li, P. Li, J.-C. Kang, and G.-S. Chen, “Collaborative filtering recommendation algorithm based on cloud model,” Journal of Software, vol. 18, no. 10, pp. 2403–2411, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Dai and F. Hu, “Research and application of text classification based on incomplete information system,” Journal of Chongqing University of Posts and Telecommunications, vol. 18, no. 3, pp. 397–401, 2006. View at Google Scholar
  24. J. Dai, Z. He, and F. Hu, “A high performance algorithm for text feature automatic selection based on cloud model,” Journal of Computational Information Systems, vol. 5, no. 6, pp. 1561–1568, 2009. View at Google Scholar · View at Scopus