Abstract
Although knowledge reduction for a decision table based on discernibility function can be used widely in data classification, there are also many disadvantages needed discussing detailedly on knowledge acquisition. To make some improvement for them, firstly, the concept of a decision table simplified was put forward for removing redundant data. Then based on knowledge granulation and conditional information entropy, the definition of a new conditional entropy, which could reflect the change of decision ability objectively and equivalently and present the concepts and operations in an inconsistent decision table simplified, was given by separating the consistent objects from the inconsistent objects. Furthermore, many propositions and properties for reduction with an inequality were proposed, and a complete knowledge reduction method was implemented. Finally, the experimental results with UCI data sets show that the proposed method of knowledge reduction is an effective technique to deal with complex data sets, and can simplify the structure and improve the efficiency of data classification.
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References
Pawlak, Z.: Rough Sets and Intelligent Data Analysis. International Journal of Information Sciences 147, 1–12 (2002)
Shannon, C.E.: The Mathematical Theory of Communication. The Bell System Technical Journal 27(3/4), 373–423 (1948)
Wang, G.Y., Zhao, J., An, J.J., Wu, Y.: A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction. Fundamenta Informaticae 68(3), 289–301 (2005)
Wang, G.Y., Yu, H., Yang, D.C.: Decision Table Reduction Based on Conditional Information Entropy. Journal of Computers 25(7), 759–766 (2002)
Guan, J.W., Bell, D.A.: Rough Computational Methods for Information Systems. International Journal of Artificial Intelligences 105, 77–103 (1998)
Miao, D.Q., Hu, G.R.: A Heuristic Algorithm for Reduction of Knowledge. Journal of Computer Research and Development 36(6), 681–684 (1999)
Liu, S.H., Sheng, Q.J., Wu, B., et al.: Research on Efficient Algorithms for Rough Set Methods. Journal of Computers 26(5), 524–529 (2003)
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, Q.H., Li, F., et al.: An Efficient Knowledge Reduction Algorithm Based on New Conditional Information Entropy. Control and Decision 20(8), 878–882 (2005)
Han, J.C., Hu, X.H., Lin, T.Y.: An Efficient Algorithm for Computing Core Attributes in Database Systems. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds.) ISMIS 2003. LNCS, vol. 2871, pp. 663–667. Springer, Heidelberg (2003)
Jiang, S.Y., Lu, Y.S.: Two New Reduction Definitions of Decision Table. Mini-Micro Systems 27(3), 512–515 (2006)
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© 2009 Springer-Verlag Berlin Heidelberg
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Xu, J., Sun, L. (2009). Research of Knowledge Reduction Based on New Conditional Entropy. In: Wen, P., Li, Y., Polkowski, L., Yao, Y., Tsumoto, S., Wang, G. (eds) Rough Sets and Knowledge Technology. RSKT 2009. Lecture Notes in Computer Science(), vol 5589. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02962-2_18
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DOI: https://doi.org/10.1007/978-3-642-02962-2_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02961-5
Online ISBN: 978-3-642-02962-2
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