Abstract
We describe statistical and empirical rule quality formulas and present an empirical comparison of them on standard machine learning datasets. From the experimental results, a set of formula-behavior rules are generated which show relationships between a formula’s performance and dataset characteristics. These formula-behavior rules are combined into formula-selection rules which can be used in a rule induction system to select a rule quality formula before rule induction.
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An, A., Cercone, N. (1999). An Empirical Study on Rule Quality Measures. 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_59
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DOI: https://doi.org/10.1007/978-3-540-48061-7_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-66645-5
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