An Efficient Attribute Reduction Algorithm
Attribute reduction is an important issue of data mining. It is generally regarded as a preprocessing phase that alleviates the curse of dimensionality, though it also leads to classificatory analysis of decision tables. In this paper, we propose an efficient algorithm TWI-SQUEEZE that can find a minimal (or irreducible) attribute subset, which preserves classificatory consistency after two scans of a decision table. Its worst-case computational complexity is analyzed. The outputs of the algorithm are two different kinds of classifiers. One is an IF-THEN rule system. The other is a decision tree.
KeywordsAttribute Reduction Decision Table Decision Attribute Conditional Attribute Discernibility Matrix
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- 4.Komorowski, J., Pawlak, Z., Polkowski, L., Skowron, A.: Rough Sets: A Tutorial. In: Rough Fuzzy Hybridization –A New Trend in Decision Making, pp. 3–98. Springer, Heidelberg (1998)Google Scholar
- 5.Skowron, A., Rauszer, C.: The Discernibility Matrices and Functions in Information System, Intelligent Decision Support-Handbook of Applications and Advances of the Rough Set Theory. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
- 6.Hoa, N.S., Son, N.H.: Some efficient algorithms for rough set methods. In: Proceedings of the Conference of Information Processing and Management of Uncertainty in Knowledge-Based Systems (1996)Google Scholar
- 7.Hu, Q., Pao, W., Yu, D.: Improved reduction algorithm based on A-Priori. Computer Science 29, 115–117 (2002)Google Scholar
- 8.Best, J.B.: Cognitive Psychology. Heinle and Heinle Publishers, Boston (1998)Google Scholar
- 9.Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2000)Google Scholar
- 11.Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases, http://www.ics.uci.edu/~mlearn/MLRepository.html