About a Fuzzy Distance between Two Fuzzy Partitions and Application in Attribute Reduction Problem

Cao Chinh Nghia 1 , Demetrovics Janos 2 , Nguyen Long Giang 3 , and Vu Duc Thi 4
  • 1 People’s Police Academy, Viet Nam
  • 2 Institute for Computer and Control (SZTAKI) Hungarian Academy of Sciences, Hungary
  • 3 Institute of Information Technology, VAST, Viet Nam
  • 4 Institute of Information Technology, VNU, Viet Nam

Abstract

According to traditional rough set theory approach, attribute reduction methods are performed on the decision tables with the discretized value domain, which are decision tables obtained by discretized data methods. In recent years, researches have proposed methods based on fuzzy rough set approach to solve the problem of attribute reduction in decision tables with numerical value domain. In this paper, we proposeafuzzy distance between two partitions and an attribute reduction method in numerical decision tables based on proposed fuzzy distance. Experiments on data sets show that the classification accuracy of proposed method is more efficient than the ones based fuzzy entropy.

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  • 1. Dubois, D., H. Prade. Rough Fuzzy Sets and Fuzzy Rough Sets. - International Journal of General Systems, Vol. 17, 1990, pp. 191-209.

  • 2. Demetrovics, J., V.D. Thi, N. L. Giang. An Efficient Algorithm for Determining the Set of All Reductive Attributes in Incomplete Decision Tables. - Cybernetics and Information Technologies, Vol. 13, 2013, No 4, pp. 118-126.

  • 3. Demetrovics, J., V.D. Thi, N.L. Giang. On Finding All Reducts of Consistent Decision Tables. - Cybernetics and Information Technologies, Vol. 14, 2014, No 4, pp. 3-10.

  • 4. Demetrovics, J., N. Thi, L. Huong, V.D. Thi, N.L. Giang. Metric Based Attribute Reduction Method in Dynamic Decision Tables. - Cybernetics and Information Technologies, Vol. 16, 2016, No 2, pp. 3-15.

  • 5. Tsang, E.C.C., D.G. Chen, D.S. Yeung, X.Z. Wang, J.W.T. Lee. Attributes Reduction Using Fuzzy Rough Sets. - IEEE Trans. Fuzzy Syst., Vol. 16, 2008, pp. 1130-1141.

  • 6. Dai, J., Q. Xu. Attribute Selection Based on Information Gain Ratio in Fuzzy Rough Set Theory with Application to Tumor Classification. - Applied Soft Computing, Vol. 13, 2013, pp. 211-221.

  • 7. Hu, Q., D.R. Yu, Z. X. Xie. Information-Preserving Hybrid Data Reduction Based on Fuzzy- Rough Techniques. - Pattern Recognit. Lett., Vol. 27, 2006, No 5, pp. 414-423.

  • 8. Hu, Q., Z.X. Xie, D.R. Yu. Hybrid Attribute Reduction Based ona Novel Fuzzy-Rough Model and Information Granulation. - Pattern Recognit., Vol. 40, 2007, pp. 3509-3521.

  • 9. Jensen, R., Q. Shen. Semantics-Preserving Dimensionality Reduction: Rough and Fuzzy- Rough-Based Approaches. - IEEE Trans. Knowl. Data Eng., Vol. 16, 2004, No 12, pp. 1457-1471.

  • 10. Jensen, R., Q. Shen. Fuzzy-Rough Attribute Reduction with Application to Web Categorization. - Fuzzy Sets Syst., Vol. 141, 2004, pp. 469-485.

  • 11. Jensen, R., Q. Shen. Fuzzy-Rough Sets Assisted Attribute Reduction. - IEEE Trans. Fuzzy Syst., Vol. 15, 2007, No 1, pp. 73-89.

  • 12. Jensen, R., Q. Shen. New Approaches to Fuzzy-Rough Feature Selection. - IEEE Trans. Fuzzy Syst., Vol. 17, 2009, No 4, pp. 824-838.

  • 13. Bhatt, R. B., M. Gopal. On Fuzzy-Rough Sets Approach to Feature Selection. - Pattern Recognit. Lett., Vol. 26, 2005, pp. 965-975.

  • 14. Qian, Y.H., Q. Wang, H.H. Cheng, J.Y. Liang, C.Y. Dang. Fuzzy-Rough Feature Selection Accelerator. - Fuzzy Sets and Systems, Vol. 258, 2015, pp. 61-78.

  • 15. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning about Data. London, Kluwer Academic Publisher, 1991.

  • 16. Pawlak, Z., J.W. Grzymala-Busse, R. Slowiski, W. Ziako. Rough Sets. Commun. - ACM, Vol. 38, 1995, No 11, pp. 89-95.

  • 17. The UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets.html

  • 18. https://sourceforge.net/projects/weka/

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