Abstract
Reduction is one of the key problem in rough set theory due to its applications in data mining, rule induction, classification, etc.. In this paper the reductions for a set-valued decision information system(DIS) are studied. The judgment theorem and the discernibility matrix of the generalized decision reduct in a set-valued DIS are given, and the relationships among the generalized decision reduct and alternative types of knowledge reduction in set-valued DIS are investigated. It is proved that the reduct in the consistent set-valued DIS is equivalent to the generalized decision reduct, and the possible reduct in the inconsistent set-valued DIS is equivalent to the generalized decision reduct. The judgment theorem and the discernibility matrix associated with the possible reduct are also established.
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References
Bazan, J.: A Comparison of Dynamic and Non-dynamic Rough Set Methods for Extracting Laws From Decision Tables. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 321–365. Physica-Verlag, Heidelberg (1998)
Kryszkiewicz, M.: Comparative Study of Alternative Types of knowledge Reduction in inconsistent systems. International Journal of Intelligent Systems 16, 105–120 (2001)
Liu, Q., Liu, S.H., Zheng, F.: Rough Logic and its Applications in Data Reduction. Journal of Software 12, 415–419 (2001) (in Chinese)
Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to Knowledge Reduction Based on Variable Precision Rough Set Model. Information Sciences 159, 255–272 (2004)
Miao, D.Q., Hu, G.R.: A Heuristic Algorithm for Reduction of Knowledge. Journal of Computer Research and Development 36, 681–684 (1999) (in Chinese)
Nguyen, H.S., Ślęzak, D.: Approximate reducts and association rules. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS (LNAI), vol. 1711, pp. 137–145. Springer, Heidelberg (1999)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Boston (1991)
Wang, G.Y. (ed.): Rough Set Theory and Knowledge acquirement. Xi’an JiaoTong University Press, Xi’an (2001) (in chinese)
Yao, Y.Y.: Generalized rough Set Models. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 286–318. Physica-Verlag, Heidelberg (1998)
Zhang, W.X., Leung, Y., Wu, W.Z. (eds.): Information Systems and knowledge Discovery (in chinese). Science Press, Beijing (2003)
Ziarko, W.: Variable Precision Rough Set Model. Journal of Computer and System Science 46, 39–59 (1993)
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Song, XX., Zhang, WX. (2006). Knowledge Reduction in Set-Valued Decision Information System. In: Greco, S., et al. Rough Sets and Current Trends in Computing. RSCTC 2006. Lecture Notes in Computer Science(), vol 4259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11908029_37
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DOI: https://doi.org/10.1007/11908029_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47693-1
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