A Hybrid Filter-Wrapper Approach for FeatureSelection

Feature selection is the task of selecting a small subset from original features that can achieve maximum classification accuracy. This subset of features has some very important benefits like, it reduces computational complexity of learning algorithms, saves time, improve accuracy and the selected features can be insightful for the people involved in problem domain. This makes feature selection as an indispensable task in classification task. This dissertation presents a two phase approach for feature selection. In the first phase a filter method is used with “correlation coefficient” and “mutual information” as statistical measure of similarity. This phase helps in improving the classification performance by removing redundant and unimportant features. A wrapper method is used in the second phase with the sequential forward selection and sequential backward elimination. This phase helps in selecting relevant feature subset that produce maximum accuracy according to the underlying classifier. The Support Vector Machine (SVM) classifier (linear and nonlinear) is used to evaluate the classification accuracy of our approach. This empirical results of commonly used data sets from the University of California, Irvine repository and microarray data sets showed that the proposed method performs better in terms of classification accuracy, number of selected features, and computational efficiency.