Dynamic Reducts and Statistical Inference

We apply rough set methods and boolean reasoning for knowledge discovery from decision tables. It is often impossible to extract general laws from experimental data by computing rst all reducts (Pawlak 1991) of a data table (decision table) and next decision rules from these reducts. We have developed an idea of dynamic reducts as a tool allowing to nd relevant reducts for the decision rule generation (Bazan 1994a), (Bazan 1994b), (Bazan 1994c), (Nguyen 1993). Tests on several data tables are showing that the application of dynamic reducts leads to the increasing of the classiication quality and/or decreasing of the size of decision rule sets. In this paper we present some statistical arguments showing that the introduced stability coeecients of dynamic reducts are proper measures of their quality.

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