Feature Subset Selection using Cascaded GA and CFS: A Filter Approach in Supervised Learning

edical data mining has enormous potential for exploring the hidden patterns in the data sets of the medical domain. These patterns can be utilized by the physicians to improve clinical diagnosis. Feature subset selection is one of data preprocessing step, which is of immense importance in the field of data mining. As a part of feature subset selection step of data preprocessing, a filter approach with genetic algorithm (GA) and Correlation based feature selection has been used in a cascaded fashion. GA rendered global search of attributes with fitness evaluation effected by CFS. Experimental results signify that the feature subset recognized by the proposed filter GA+CFS, when given as input to five classifiers, namely decision tree, Naive Bayes, Bayesian, Radial basis function and k-nearest neighbor classifiers showed enhanced classification accuracy. Experiments have been carried out on four medical data sets publicly available at UCI.