Feature Selection Based on Relative Attribute Dependency: An Experimental Study

Most existing rough set-based feature selection algorithms suffer from intensive computation of either discernibility functions or positive regions to find attribute reduct. In this paper, we develop a new computation model based on relative attribute dependency defined as the proportion of the projection of the decision table on a condition attributes subset to the projection of the decision table on the union of the condition attributes subset and the decision attributes set. To find an optimal reduct, we use information entropy conveyed by the attributes as the heuristic. A novel algorithm to find optimal reducts of condition attributes based on the relative attribute dependency is implemented using Java, and is experimented with 10 data sets from UCI Machine Learning Repository. We conduct the comparison of data classification using C4.5 with the original data sets and their reducts. The experiment results demonstrate the usefulness of our algorithm.