Modified Binary Bat Algorithm for Feature Selection in Unsupervised Learning

Feature selection is the process of selecting a subset of optimal features by removing redundant and irrelevant features. In supervised learning, feature selection process uses class label. But feature selection is difficult in unsupervised learning since class labels are not present. In this paper, we present a wrapper based unsupervised feature selection method with the modified binary bat approach with k-means clustering algorithm. To ensure diversification in the search space, mutation operator is introduced in the proposed algorithm. To validate the selected features by our method, classification algorithms like decision tree induction, Support Vector Machine and Naïve Bayesian classifier are used. The results show that the proposed method identifies a minimal number of features with improved accuracy when compared with the other methods.