Considering that the existing discretization algorithms do not give simultaneously attention to evolution speed
and solution’s quality, a new class-attribute interdependency maximization based algorithm for supervised discretization
is proposed in this paper. The algorithm considers the distribution of both class and continuous attributes, and according
to the underlying correlation structure of them, the discretization scheme is constructed which can maintain the highest
interdependence between the target class and all the discretized attributes. The experiment results show that, with a reasonable
execution time, the proposed algorithm can improve the accuracy of the classification result and reduce the number of
classification rules.