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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References
Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction. Kluwer Academic Publishers, Dordrecht (2002)
Miao, D.Q., Wang, G.Y., Liu, Q., Lin, T.Y., Yao, Y.Y.: Granular Computing: Past, Present, and the Future Perspectives. Academic Press, Beijing (2007)
Xu, J.C., Sun, L.: New Reduction Algorithm Based on Decision Power of Decision Table. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds.) RSKT 2008. LNCS (LNAI), vol. 5009, pp. 180–188. Springer, Heidelberg (2008)
Xu, J.C., Sun, L.: Research of Knowledge Reduction Based on New Conditional Entropy. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds.) RSKT 2009. LNCS, vol. 5589, pp. 144–151. Springer, Heidelberg (2009)
Yao, Y.Y.: A Partition Model of Granular Computing. LNCS Transactions on Rough Sets 1, 232–253 (2004)
Lin, T.Y., Louie, E.: Finding Association Rules by Granular Computing: Fast Algorithms for Finding Association Rules. In: Proceedings of the 12th International Conference on Data Mining, Rough Sets and Granular Computing, Berlin, German, pp. 23–42 (2002)
Kryszkiewicz, M.: Comparative Study of Alternative Types of Knowledge Reduction in Insistent Systems. International Journal of Intelligent Systems 16, 105–120 (2001)
Hu, Q.H., Yu, D.R., Xie, Z.X.: Neighborhood Classifiers. Expert Systems with Applications 34, 866–876 (2008)
Xu, Z.Y., Liu, Z.P., et al.: A Quick Attribute Reduction Algorithm with Complexity of Max(O(|C||U|),O(|C| 2|U/C|)). Journal of Computers 29(3), 391–399 (2006)
Liu, Y., Xiong, R., Chu, J.: Quick Attribute Reduction Algorithm with Hash. Chinese Journal of Computers 32(8), 1493–1499 (2009)
Liu, S.H., Sheng, Q.J., Wu, B., et al.: Research on Efficient Algorithms for Rough Set Methods. Chinese Journal of Computers 26(5), 524–529 (2003)
Guan, J.W., Bell, D.A.: Rough Computational Methods for Information Systems. International Journal of Artificial Intelligences 105, 77–103 (1998)
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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
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