Abstract
In this paper, we firstly introduce a concept of inconsistency classification based on which we draw a qualitative conclusion that the approach by Hu and Cercone for computing an attribute core based on Skowron’s discernibility matrix is correct for both consistent and partially inconsistent decision tables, but may fail to work for entirely inconsistent ones. Secondly, we improve the work of Zhi and Miao concerning the computation of core attributes by defining a new binary discernibility matrix. Finally, as another application of inconsistency classification, we show that an attribute core from the algebra view is equivalent to that from the information view not only for consistent but also for partially inconsistent decision tables.
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References
Pawlak Z., Slowinski K.: Rough Set Approach to Multi-attribute Decision Analysis. European Journal of Operational Research, 72(1994) 443–459
Hu X., Cercone N.: Learning in Relational Databases: a Rough Set Approach. J. Computational Intelligence, 2(1995) 323–337
Ye D.Y., Chen Z.J.: A New Discenibility Matrix and the Computation of a Core. Acta Electronica Sinica, 30(2002) 1086–1088
Zhi T.Y., Miao D.Q.: The Binary Discernibility Matrix’s Transformation and High Efficiency Attributes Reduction Algorithm’s Conformation. Computer Science, 29(2002) 140–142
Felix R., Ushio T.: Rough Sets Based Machine Learning Using a Binary Discernibility Matrix. IPMM’s 99 Published, (1999) 299–305
Wang G.Y.: Attribute Core of Decision Table. Lecture Notes in Artificial Intelligence, Vol. 2475. Springer-Verlag, Berlin Heidelberg New York(2002) 213–217
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Ye, D., Chen, Z. (2003). Inconsistency Classification and Discernibility-Matrix-Based Approaches for Computing an Attribute Core. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_36
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DOI: https://doi.org/10.1007/3-540-39205-X_36
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