Abstract
Rough set theory has mainly been applied to data with categorical values. In order to handle data with numerical values, we have defined numerical patterns with two symbols # and @, and have proposed more flexible rough sets based rule generation. The concepts of ‘coarse’ and ‘fine’ for rules are explicitly defined according to numerical patterns. This paper focuses on the rough sets based method for rule generation, which is enhanced by numerical patterns, and refers to the tool programs. Tool programs are applied to data in UCI Machine Learning Repository, and some useful rules are obtained.
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Sakai, H., Koba, K., Ishibashi, R., Nakata, M. (2007). On a Rough Sets Based Tool for Generating Rules from Data with Categorical and Numerical Values. In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_26
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DOI: https://doi.org/10.1007/978-3-540-73729-2_26
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