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The Scientific World Journal
Volume 2014, Article ID 517498, 7 pages
http://dx.doi.org/10.1155/2014/517498
Research Article

Simple-Random-Sampling-Based Multiclass Text Classification Algorithm

1Department of Language Engineering, PLA University of Foreign Languages, Luoyang, Henan 471003, China
2College of Computer, National University of Defense Technology, Changsha, Hunan 410073, China
3College of Humanities and Social Sciences, National University of Defense Technology, Changsha, Hunan 410073, China

Received 6 December 2013; Accepted 11 February 2014; Published 19 March 2014

Academic Editors: F. Yu and G. Yue

Copyright © 2014 Wuying Liu 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. 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. McKinsey, “The challenge and opportunity of ‘big data’,” 2011.
  3. M. E. J. Newman, “Power laws, Pareto distributions and Zipf's law,” Contemporary Physics, vol. 46, no. 5, pp. 323–351, 2005. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Tan, X. Cheng, M. M. Ghanem, B. Wang, and H. Xu, “A novel refinement approach for text categorization,” in Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM '05), pp. 469–476, November 2005. View at Scopus
  5. W. Zhao, S. Tang, and W. Dai, “An improved kNN algorithm based on essential vector,” Electronics & Electrical Engineering, vol. 7, no. 123, pp. 119–122, 2012. View at Google Scholar
  6. E. Han and G. Karypis, “Centroid-based document classification: analysis & experimental results,” in Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 424–431, 2000.
  7. T. Zhang, “Regularized winnow methods,” in Advances in Neural Information Processing Systems, vol. 13, pp. 703–709, 2000. View at Google Scholar
  8. W. Liu and T. Wang, “Multi-field learning for email spam filtering,” in Proceedings of the 33rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '10), pp. 745–746, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Review, vol. 51, no. 4, pp. 661–703, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Drucker, D. Wu, and V. N. Vapnik, “Support vector machines for spam categorization,” IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1048–1054, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Liu and T. Wang, “Online active multi-field learning for efficient email spam filtering,” Knowledge and Information Systems, vol. 33, no. 1, pp. 117–136, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. D. W. Aha, D. Kibler, and M. K. Albert, “Instance-based learning algorithms,” Machine Learning, vol. 6, no. 1, pp. 37–66, 1991. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Zobel and A. Moffat, “Inverted files for text search engines,” ACM Computing Surveys, vol. 38, no. 2, article 6, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. C. van Rijsbergen, Information Retrieval, Butterworths, London, UK, 1979.