Comparative Study of Attribute Selection Using Gain Ratio and Correlation Based Feature Selection

Feature subset selection is of great importance in the field of data mining. The high dimension data makes testing and training of general classification methods difficult. In the present paper two filters approaches namely Gain ratio and Correlation based feature selection have been used to illustrate the significance of feature subset selection for classifying Pima Indian diabetic database (PIDD). The C4.5 tree uses gain ratio to determine the splits and to select the most important features. Genetic algorithm is used as search method with Correlation based feature selection as subset evaluating mechanism. The feature subset obtained is then tested using two supervised classification method namely, Back propagation neural network and Radial basis function network. Experimental results show that the feature subsets selected by CFS filter resulted in marginal improvement for both back propagation neural network and Radial basis function network classification accuracy when compared to feature subset selected by information gain filter.