Abstract
Efficient knowledge reduction in large inconsistent decision information systems is a challenging problem. Moreover, existing approaches have still their own limitations. To address these problems, in this article, by applying the technique of granular computing, provided some rigorous and detailed proofs, and discussed the relationship between granular reduct introduced and knowledge reduction based on positive region related to simplicity decision information systems. By using radix sorting and hash methods, the object granules as basic processing elements were employed to investigate knowledge reduction. The proposed method can be applied to both consistent and inconsistent decision information systems.
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Sun, L., Xu, J., Li, S. (2010). Knowledge Reduction Based on Granular Computing from Decision Information Systems. In: Yu, J., Greco, S., Lingras, P., Wang, G., Skowron, A. (eds) Rough Set and Knowledge Technology. RSKT 2010. Lecture Notes in Computer Science(), vol 6401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16248-0_12
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DOI: https://doi.org/10.1007/978-3-642-16248-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16247-3
Online ISBN: 978-3-642-16248-0
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