Generation of approximation rules using information gain

Suggests a method for generating approximation rules by the information gain used in the machine learning by decision tree. We studied that these rules are better than other approximation rules induced by using /spl chi//sup 2/ goodness of fittest or dependency of attributes in rough set theory by an experiment using neural network.