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

1. Dubois, D., H. Prade. Rough Fuzzy Sets and Fuzzy Rough Sets. - International Journal of General Systems, Vol. 17, 1990, pp. 191-209.10.1080/03081079008935107Search in Google Scholar

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. 118-126.10.2478/cait-2013-0058Search in Google Scholar

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. 3-10.10.1515/cait-2014-0001Search in Google Scholar

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. 3-15.10.1515/cait-2016-0016Search in Google Scholar

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. 1130-1141.10.1109/TFUZZ.2006.889960Search in Google Scholar

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. 211-221.10.1016/j.asoc.2012.07.029Search in Google Scholar

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. 414-423.10.1016/j.patrec.2005.09.004Search in Google Scholar

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. 3509-3521.10.1016/j.patcog.2007.03.017Search in Google Scholar

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. 1457-1471.10.1109/TKDE.2004.96Search in Google Scholar

10. Jensen, R., Q. Shen. Fuzzy-Rough Attribute Reduction with Application to Web Categorization. - Fuzzy Sets Syst., Vol. 141, 2004, pp. 469-485.10.1016/S0165-0114(03)00021-6Search in Google Scholar

11. Jensen, R., Q. Shen. Fuzzy-Rough Sets Assisted Attribute Reduction. - IEEE Trans. Fuzzy Syst., Vol. 15, 2007, No 1, pp. 73-89.10.1109/TFUZZ.2006.889761Search in Google Scholar

12. Jensen, R., Q. Shen. New Approaches to Fuzzy-Rough Feature Selection. - IEEE Trans. Fuzzy Syst., Vol. 17, 2009, No 4, pp. 824-838.10.1109/TFUZZ.2008.924209Search in Google Scholar

13. Bhatt, R. B., M. Gopal. On Fuzzy-Rough Sets Approach to Feature Selection. - Pattern Recognit. Lett., Vol. 26, 2005, pp. 965-975.10.1016/j.patrec.2004.09.044Search in Google Scholar

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. 61-78.10.1016/j.fss.2014.04.029Search in Google Scholar

15. Pawlak, Z. Rough Sets: Theoretical Aspects of Reasoning about Data. London, Kluwer Academic Publisher, 1991.Search in Google Scholar

16. Pawlak, Z., J.W. Grzymala-Busse, R. Slowiski, W. Ziako. Rough Sets. Commun. - ACM, Vol. 38, 1995, No 11, pp. 89-95.10.1145/219717.219791Search in Google Scholar

17. The UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets.htmlSearch in Google Scholar

18. https://sourceforge.net/projects/weka/ Search in Google Scholar