Feature selection based on rough set and information entropy

Most existing rough set-based feature selection algorithms suffer from intensive computation of either discernibility functions or positive regions to find attribute reduct In order to improve the efficiency, 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 Two novel algorithms to find optimal reducts of condition attributes based on the relative attribute dependency are implemented using Java 1.4, and are experimented with 10 data sets from UCI machine learning repository. The experiment results demonstrate their usefulness and are analyzed for further research.