Abstract
We explore an extension of rough set theory based on probability theory. Lower and upper approximations, the basic ideas of rough set theory, are generalized by adding two parameters, denoted by alpha and beta. In our experiments, for different pairs of alpha and beta, we induced three types of rules: positive, boundary, and possible. The quality of these rules was evaluated using ten-fold cross-validation on five data sets. The main results of our experiments are that there is no significant difference in quality between positive and possible rules and that boundary rules are the worst.
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Grzymała-Busse, J.W., Marepally, S.R., Yao, Y. (2013). An Empirical Comparison of Rule Sets Induced by LERS and Probabilistic Rough Classification. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30344-9_9
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DOI: https://doi.org/10.1007/978-3-642-30344-9_9
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