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
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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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Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
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