Abstract
Rough Sets theory provides a method of estimating and/or inducing knowledge structure of if-then rules from various databases, using approximations of accuracy and coverage indices. Several recent studies have examined the confidence of these indices. In these studies their estimated rules were based on a sample data set obtained from a population, and the sampling affects the confidence of the estimation. However, these studies of the quality of the approximation evaluate the effects on rule estimation indirectly. In this paper, we propose a new rule induction method by statistical testing which directly contains the effect of sampling. The validity and usefulness of our method are confirmed by a computer simulation experiment and comparison of the results with those by other well-known methods.
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Matsubayashi, T., Kato, Y., Saeki, T. (2012). A New Rule Induction Method from a Decision Table Using a Statistical Test. In: Li, T., et al. Rough Sets and Knowledge Technology. RSKT 2012. Lecture Notes in Computer Science(), vol 7414. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31900-6_11
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DOI: https://doi.org/10.1007/978-3-642-31900-6_11
Publisher Name: Springer, Berlin, Heidelberg
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