Abstract
Most of the existing discretization approaches discrete each continuous attribute independently, without considering the discretization results of other continuous attributes. Therefore, some unreasonable and superfluous discretization split points are usually created. Based on compatibility rough set model and genetic algorithm, a global discretization approach has been provided. The experimental results indicate that the global discretization approach proposed can significantly decrease the number of discretization split points and the number of rules, but increase the predictive accuracy of the classifier.
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© 1999 Springer-Verlag Berlin Heidelberg
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Sun, L., Gao, W. (1999). The Discretization of Continuous Attributes Based on Compatibility Rough Set and Genetic Algorithm. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_23
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DOI: https://doi.org/10.1007/978-3-540-48061-7_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
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