Abstract
This paper presents the rule generation method based on evolutionary computation and rough set, which integrates the procedure of discretization and reduction using information entropy-based uncertainty measures and evolutionary computation. Based on the definitions of certain rules and approximate certain rules, the paper focuses on the reduction by meanings of evolutionary computation. Experimental results reveal that the proposed method leads to better classification quality and smaller number of decision rules comparing with other methods.
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Shang, L., Wan, Q., Zhao, ZH., Chen, SF. (2005). Evolutionary Computation and Rough Set-Based Hybrid Approach to Rule Generation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3612. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539902_105
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DOI: https://doi.org/10.1007/11539902_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28320-1
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