Decision Rules Mining Method by Integrating Feature Selection and Discretization
与特征选取和离散化集成的决策规则挖掘方法

Decision rules can be mined from given data using rough set theory. The continuous features must be discretized. Removing the redundant feature attributes and selecting the useful feature subset can simplify the decision rules. We construct a genetic algorithm for decision rules mining integrated by feature selection and discretization using an entropy based uncertainty measure. The usefulness of the proposed method is demonstrated by the experimental results.