Table of Contents Author Guidelines Submit a Manuscript
The Scientific World Journal
Volume 2014, Article ID 359626, 7 pages
http://dx.doi.org/10.1155/2014/359626
Research Article

A Variable Precision Attribute Reduction Approach in Multilabel Decision Tables

1Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, Shanxi 030006, China
2Department of Mathematics and Physics, Shijiazhuang Tiedao University, Shijiazhuang, Hebei 050043, China
3School of Computer and Information Technology, Shanxi University, Taiyuan, Shanxi 030006, China

Received 18 June 2014; Accepted 17 July 2014; Published 6 August 2014

Academic Editor: Yunqiang Yin

Copyright © 2014 Hua Li 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. R. E. Schapire and Y. Singer, “BoosTexter: a boosting-based system for text categorization,” Machine Learning, vol. 39, no. 2-3, pp. 135–168, 2000. View at Publisher · View at Google Scholar · View at Scopus
  2. M. R. Boutell, J. Luo, X. Shen, and C. M. Brown, “Learning multi-label scene classification,” Pattern Recognition, vol. 37, no. 9, pp. 1757–1771, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Elisseeff and J. Weston, “A kernel method for multi-labelled classification,” in Advances in Neural Information Processing Systems 14, 2002. View at Google Scholar
  4. Z. Pawlak, “Rough sets,” International Journal of Computer and Information Sciences, vol. 11, no. 5, pp. 341–356, 1982. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. K. Thangavel and A. Pethalakshmi, “Dimensionality reduction based on rough set theory: a review,” Applied Soft Computing Journal, vol. 9, no. 1, pp. 1–12, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. C. Wu, Y. Yue, M. Li, and O. Adjei, “The rough set theory and applications,” Engineering Computations, vol. 21, no. 5, pp. 488–511, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. J. W. Grzymała-Busse, Managing Uncertainty in Expert Systems, Kluwer Academic Publishers, 1991.
  8. J. W. Grzymała-Busse, “LERS—a system for learning from examples based on rough sets,” in Intelligent Decision Support: Handbook of Applications and Advances of the Rough Set Theory, pp. 3–18, Kluwer Academic Publishers, New York, NY, USA, 1992. View at Google Scholar
  9. W. Ziarko, “Variable precision rough set model,” Journal of Computer and System Sciences, vol. 46, no. 1, pp. 39–59, 1993. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  10. M. Kryszkiewicz, “Comparative study of alternative types of knowledge reduction in inconsistent systems,” International Journal of Intelligent Systems, vol. 16, no. 1, pp. 105–120, 2001. View at Google Scholar
  11. D. Li, B. Zhang, and Y. Leung, “On knowledge reduction in inconsistent decision information systems,” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 12, no. 5, pp. 651–672, 2004. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  12. A. Skowron and C. Rauszer, “The discernibility matrices and functions in information systems,” in Intelligent Decision Support—Handbook of Applications and Advances of the Rough Sets Theory, pp. 331–362, Kluwer Academic Publishers, 1992. View at Google Scholar
  13. Z. Xu, Z. Liu, B. Yang, and W. Song, “A quick attribute reduction algorithm with complexity of max(o(|c||u|);o(|c|2|u/c|)),” Chinese Journal of Computers, vol. 29, no. 3, pp. 391–398, 2006. View at Google Scholar
  14. Y. Qian, J. Liang, W. Pedrycz, and C. Dang, “Positive approximation: an accelerator for attribute reduction in rough set theory,” Artificial Intelligence, vol. 174, no. 9-10, pp. 597–618, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet · View at Scopus
  15. N. Ghamrawi and A. McCallum, “Collective multi-label classification,” in Proceedings of the 14th ACM International Conference on Information and Knowledge Management (CIKM '05), pp. 195–200, New York, NY, USA, November 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Kiritchenko, Hierarchical text categorization and its application to bioinformatics [Ph.D. thesis], Queen's University, Kingston, Canada, 2005.
  17. J. Read, Scalable multi-label classification [Ph.D. thesis], University of Waikato, Hamilton, New Zealand, 2010.