Fuzzy Distance Based Attribute Reduction in Decision Tables

  • Cao Chinh Nghia Tạp chí CNTT&TT
  • Vu Duc Thi
  • Nguyen Long Giang
  • Tan Hanh
Keywords: Fuzzy rough set, fuzzy decision table, fuzzy equivalence relation, fuzzy distance, attribute reduction, reduct


In recent years, fuzzy rough set based attribute reduction has attracted the interest of many researchers. The attribute reduction methods can perform directly on the decision tables with numerical attribute value domain. In this paper, we propose a fuzzy distance based attribute reduction method on the decision table with numerical attribute value domain. Experiments on data sets show that the proposed method is more efficient than the ones based on Shannon’s entropy on the executed time and the classification accuracy of reduct.

Author Biography

Cao Chinh Nghia, Tạp chí CNTT&TT
thông tin di động, MIMO


CHEN D. G., LEI Z., SUYUN Z., QING H. H. and PENG F. Z., A Novel Algorithm for Finding Reducts With Fuzzy Rough Sets, IEEE Transaction on Fuzzy Systems, Vol. 20, No. 2, 2012, pp. 385-389.

CHENG Y., Forward approximation and backward approximation in fuzzy rough sets, Neurocomputing, Volume 148, 2015, pp. 340-353.

DUBOIS D., PRADE H., Putting rough sets and fuzzy sets together, Intelligent Decision Support, Kluwer Academic Publishers,Dordrecht, 1992.

DUBOIS D., PRADE H., Rough fuzzy sets and fuzzy rough sets, International Journal of General Systems, 17, 1990, pp. 191-209.

DAI J. H., XU Q., Attribute selection based on information gain ratio in fuzzy rough set theory with application to tumor classification, Applied Soft Computing 13, 2013, pp. 211-221.

HE Q., WU C. X., CHEN D. G., ZHAO S. Y., Fuzzy rough set based attribute reduction for information systems with fuzzy decisions, Knowledge-Based Systems 24, 2011, pp. 689-696.

HU Q. H., YU D. R., XIE Z. X., Information-preserving hybrid data reduction based on fuzzy-rough techniques, Pattern Recognition Letters 27, 2006, pp. 414-423.

HU Q. H., YU D. R., Fuzzy Probability Approximation Space and Its Information Measures, IEEE Transaction on Fuzzy Systems, Vol 14, 2006.

JENSEN R., SHEN Q., Fuzzy-Rough Sets for Descriptive Dimensionality Reduction, Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE'02, 2002, pp. 29-34.

JENSEN R., SHEN Q., Fuzzy–rough attribute reduction with application to web categorization, Fuzzy Sets and Systems, Volume 141, Issue 3, 2004, pp. 469-485.

NGUYEN LONG GIANG, Rough Set Based Data Mining Methods, Doctor of Thesis, Institute of Information Technology, 2012.

PAWLAK Z., Rough sets, International Journal of Computer and Information Sciences, 11(5), 1982, pp. 341-356.

QIAN Y. H., LIANG J. Y., DANG C. Y., Knowledge structure, knowledge granulation and knowledge distance in a knowledge base, International Journal of Approximate Reasoning, 2009, pp. 174-188.

QIAN Y. H., LIANG J. Y., WEI Z., Wu Z., DANG C. Y., Information Granularity in Fuzzy Binary GrC Model, IEEE Transaction on Fuzzy Systems, Vol. 19, No. 2, 2011.

QIAN Y. H, LI Y. B., LIANG J. Y., LIN G. P., DANG C. Y., Fuzzy granular structure distance, IEEE Transactions on Fuzzy Systems, 23(6), 2015, pp.2245-2259.

TSANG E.C.C., CHEN D. G., YEUNG D.S., XI Z. W., JOHN W. T. LEE, Attributes Reduction Using Fuzzy Rough Sets, IEEE Transactions on Fuzzy Systems, Volume16, Issue 5 , 2008, pp. 1130- 1141.

XU F. F., MIAO D. Q., WEI L., An Approach for Fuzzy-Rough Sets Attributes Reduction via Mutual Information, Fourth International Conference on Fuzzy Systems and Knowledge Discovery, FSKD, 2007, Volume 3, pp. 107-112.

ZADEH L. A., Fuzzy sets, Information and Control, 8, 1965, pp. 338-353.

The UCI machine learning repository, http://archive.ics.uci.edu/ml/datasets.html


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