Abstract
This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analysis brings new insights into the problem of reduct construction, and provides guidelines for the design of new algorithms.
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References
Bazan, J.G., Nguyen, H.S., Nguyen, S.H., Synak, P., Wroblewski, J.: Rough set algorithms in classification problem. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough Set Methods and Applications, pp. 49–88 (2000)
Beaubouef, T., Petry, F.E., Arora, G.: Information-theoretic measures of uncertainty for rough sets and rough relational databases. Information Sciences 109, 185–195 (1998)
Hu, X., Cercone, N.: Learning in relational databases: a rough set approach. Computation Intelligence: An International Journal 11, 323–338 (1995)
Jenson, R., Shen, Q.: A rough set-aided system for sorting WWW bookmarks. In: Zhong, N., et al. (eds.) Web Intelligence: Research and Development, pp. 95–105 (2001)
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., Wang, J.: An information representation of the concepts and operations in rough set theory. Journal of Software 10, 113–116 (1999)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer, Boston (1991)
Shen, Q., Chouchoulas, A.: A modular approach to generating fuzzy rules with reduced attributes for the monitoring of complex systems. Engineering Applications of Artificial Intelligence 13, 263–278 (2000)
Skowron, A., Rauszer, C.: The discernibility matrices and functions in information systems. In: Slowiński, R. (ed.) Intelligent Decision Support, Handbook of Applications and Advances of the Rough Sets Theory, Kluwer, Dordrecht (1992)
Slezak, D.: Various approaches to reasoning with frequency based decision reducts: a survey. In: Polkowski, L., Tsumoto, S., Lin, T.Y. (eds.) Rough set methods and applications, pp. 235–285. Physica-verlag, Heidelberg (2000)
Wang, G., Yu, H., Yang, D.: Decision table reduction based on conditional information entropy. Chinese Journal of Computers 25, 759–766 (2002)
Wang, J., Wang, J.: Reduction algorithms based on discernibility matrix: the ordered attributes method. Journal of Computer Science and Technology 16, 489–504 (2001)
Yu, H., Yang, D., Wu, Z., Li, H.: Rough set based attribute reduction algorithm. Computer Engineering and Applications 17, 22–47 (2001)
Zhao, K., Wang, J.: A reduction algorithm meeting users’ requirements. Journal of Computer Science and Technology 17, 578–593 (2002)
Ziarko, W.: Rough set approaches for discovering rules and attribute dependencies. In: Klösgen, W., Żytkow, J.M. (eds.) Handbook of Data Mining and Knowledge Discovery, Oxford, pp. 328–339 (2002)
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Yao, Y., Zhao, Y., Wang, J. (2006). On Reduct Construction Algorithms. In: Wang, GY., Peters, J.F., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2006. Lecture Notes in Computer Science(), vol 4062. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11795131_43
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DOI: https://doi.org/10.1007/11795131_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-36297-5
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