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Attribute Reduction Algorithm Based on Improved Information Gain Rate and Ant Colony Optimization

  • Jipeng Wei
  • Qianjin Wei
  • Yimin Wen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10939)

Abstract

Solving minimal attribute reduction (MAR) in rough set theory is a NP-hard and nonlinear constrained combinatorial optimization problem. Ant colony optimization (ACO), a new intelligent computing method, takes strategies of heuristic search, which is characterized by a distributed and positive feedback and it has the advantage of excellent global optimization ability for handling combinatorial optimization problems. Having considered that the existing information entropy and information gain methods fail to help to select the optimal minimal attribute every time, this paper proposed a novel attribute reduction algorithm based on ACO. Firstly, the algorithm adopts an improved information gain rate as heuristic information. Secondly, each ant solves a problem of minimum attributes reduction and then conduct redundancy test to each selected attribute. What’s more, redundant detection of all non-core attributes in the optimal solution will be perfomed in each generation. The result of the experiment on several datasets from UCI show that the proposed algorithms are more capable of finding the minimum attribute reduction and can faster converge and at the same time they can almost keep the classification accuracy, compared with the traditional attribute reduction based on ACO algorithm.

Keywords

Rough set Ant colony optimization Information gain rate Attribute reduction 

Notes

Acknowledgements

This work has been supported by National Natural Science Foundation of China (61363029, 61572146, U1711263), Science Foundation of Guangxi Key Laboratory of Trusted Software (kx201515), and the Foundation of Guangxi Educational Committee (KY2015YB105).

References

  1. 1.
    Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11(5), 341–356 (1982)CrossRefGoogle Scholar
  2. 2.
    Ding, H., Ding, S.F., Li-Hua, H.U.: Research progress of attribute reduction based on rough sets. Comput. Eng. Sci. 32(6), 92–94 (2010). (in Chinese)Google Scholar
  3. 3.
    Jensen, R., Shen, Q.: Semantics-preserving dimensionality reduction: rough and fuzzy-rough-based approaches. IEEE Trans. Knowl. Data Eng. 16(12), 1457–1471 (2004)CrossRefGoogle Scholar
  4. 4.
    Hedar, A.R., Wang, J., Fukushima, M.: Tabu search for attribute reduction in rough set theory. Soft. Comput. 12(9), 909–918 (2008)CrossRefGoogle Scholar
  5. 5.
    Zhai, J.H., Liu, B., Zhang, S.: A feature selection approach based on rough set relative classification information entropy and particle swarm optimization. CAAI Trans. Intell. Syst. 12(3), 397–404 (2017). in ChineseGoogle Scholar
  6. 6.
    Dorigo, M., Maniezzo, V., Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man. Cybern. B 26(1), 29–41 (1996)CrossRefGoogle Scholar
  7. 7.
    Liao, T., Stützle, T., Oca, M.A.M.D., et al.: A unified ant colony optimization algorithm for continuous optimization. Eur. J. Oper. Res. 234(3), 597–609 (2014)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Duan, H., Wang, D., Yu, X.: Review on research progress in ant colony algorithm. Chin. J. Nat. 28(2), 102–105 (2006). in ChineseGoogle Scholar
  9. 9.
    Miao, D., Wang, J.: Information representation of the concepts and operations in rough set theory. J. Softw. 10(2), 113–116 (1999). (in Chinese)Google Scholar
  10. 10.
    Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowinski, R. (ed.) Intelligent Decision Support. Handbook of Applications and Advances of the Rough Set Theory, pp. 331–362. Kluwer, Dordrecht (1992)Google Scholar
  11. 11.
    Yao, Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180(3), 341–353 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Lu, J., Li, D., Zhai, Y., et al.: A model for type-2 fuzzy rough sets. Inf. Sci. 328(C), 359–377 (2016). in ChineseCrossRefGoogle Scholar
  13. 13.
    Qian, Y., Li, S., Liang, J., et al.: Pessimistic rough set based decisions: a multigranulation fusion strategy. Inf. Sci. 264(6), 196–210 (2014)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Wang, J., Miao, D.: Analysis on attribute reduction strategies of rough set. J. Comput. Sci. Technol. 13(2), 189–192 (1998). in ChineseMathSciNetCrossRefGoogle Scholar
  15. 15.
    Wong, S.K.M., Ziarko, W.: On optimal decision rules in decision tables. Bull. Pol. Acad. Sci. Math. 33(11), 693–696 (1985)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Jensen, R., Shen, Q.: Finding rough set reducts with ant colony optimization. In: Proceedings of 2003 UK Workshop on Computational Intelligence, pp. 15–22 (2003)Google Scholar
  17. 17.
    Ke, L., Feng, Z., Ren, Z.: An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn. Lett. 29(9), 1351–1357 (2008)CrossRefGoogle Scholar
  18. 18.
    Chen, Y., Chen, Y.: Attribute Reduction Algorithm Based on Information Entropy and Ant Colony Optimization. J. Chin. Comput. Syst. 36(3), 586–590 (2015). in ChineseGoogle Scholar
  19. 19.
    Chen, Y., Miao, D., Wang, R.: A rough set approach to feature selection based on ant colony optimization. Elsevier Science Inc. (2010)CrossRefGoogle Scholar
  20. 20.
    Chebrolu, S., Sanjeevi, S.G.: Attribute reduction on continuous data in rough set theory using ant colony optimization metaheuristic. In: Proceedings of International Symposium on Women in Computing and Informatics. ACM, New York, pp. 17–24 (2015)Google Scholar
  21. 21.
    Yan, Y., Yang, H.: Knowledge reduction algorithm based on mutual information. J. Tsinghua Univ. 42(2), 1903–1906 (2007). in ChineseMathSciNetGoogle Scholar
  22. 22.
    Shannon, C.E., Weaver, W., Hajek, B., et al.: The mathematical theory of communication. Phys. Today 3(9), 31–32 (1950)CrossRefGoogle Scholar
  23. 23.
    Hall, M., Frank, E., Holmes, G., et al.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and Information SafetyGuilin University of Electronic TechnologyGuilinChina
  2. 2.Guangxi Key Laboratory of Trusted SoftwareGuilin University of Electronic TechnologyGuilinChina

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