Advertisement

An Improved Ant Colony Optimization Applied to Attributes Reduction

  • Ting-quan Deng
  • Cheng-dong Yang
  • Yue-tong Zhang
  • Xin-xia Wang
Part of the Advances in Soft Computing book series (AINSC, volume 54)

Abstract

Attribute reduction problem (ARP) in rough set theory is an NP-hard problem, which is difficult to use fast traditional method to solve. In this paper, we discuss about the difference between the traveling salesman problems (TSP) and the ARP, and then we bring up a new state transition probability formula and a new pheromone traps increment formula of ant colony optimization. The results demonstrate that the improved ant colony optimization is better than initial ant colony optimization used in attribute reduction and more suitable for ARP.

Keywords

Ant Colony Optimization Attribute Reduction Rough Sets Theory 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition. Academic Press, New York (2006)zbMATHGoogle Scholar
  3. 3.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Boston (1991)zbMATHGoogle Scholar
  4. 4.
    Pawlak, Z.: Rough sets and data analysis. In: Proceedings of the Asian Fuzzy Systems Symposium, pp. 1–6 (1996)Google Scholar
  5. 5.
    Skowron, A., Pal, S.K.: Rough sets, pattern recognition, and data mining. Pattern Recognition Letters 24, 829–933 (2003)CrossRefGoogle Scholar
  6. 6.
    Swiniarski, R.W., Skowron, A.: Rough set methods in feature selection and recognition. Pattern Recognition Letters 24, 833–849 (2003)zbMATHCrossRefGoogle Scholar
  7. 7.
    Pawlak, Z., Skowron, A.: Rudiments of rough sets. Information Sciences 177, 3–27 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Wong, S.K.M., Ziarko, W.: On optional decision rules in decision tables. Bulletin of Polish Academy of Science 33, 693–696 (1985)zbMATHMathSciNetGoogle Scholar
  9. 9.
    Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowledge Data Eng. 16, 1457–1471 (2004)CrossRefGoogle Scholar
  10. 10.
    Bazan, J., Nguyen, H.S., Nguyen, S.H., Synak, P., Wróblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88. Physica-Verlag, Heidelberg (2000)Google Scholar
  11. 11.
    Wróblewski, J.: Finding minimal reducts using genetic algorithms. In: Proc. 2nd Annual Joint Conf. on Information Sciences, Wrightsville Beach, NC, pp. 186–189 (1995)Google Scholar
  12. 12.
    Liangjun, K., Zuren, F., Zhigang, R.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recognition Letters 29, 1351–1357 (2008)CrossRefGoogle Scholar
  13. 13.
    Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of UK Workshop on Computational Intelligence, pp. 15–22 (2003)Google Scholar
  14. 14.
    Wang, X., Yang, J., Teng, X., Xia, W., Jensen, R.: Feature selection based on rough sets and particle swarm optimization. Pattern Recognition Letters 28, 459–471 (2007)CrossRefGoogle Scholar
  15. 15.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: Optimization by a colony of cooperating agents. IEEE Trans. Syst. Man Cybern. B 26, 29–41 (1996)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ting-quan Deng
    • 1
  • Cheng-dong Yang
    • 1
  • Yue-tong Zhang
    • 1
  • Xin-xia Wang
    • 1
  1. 1.College of ScienceHarbin Engineering UniversityHarbinP.R. China

Personalised recommendations