Modified Binary Bat Algorithm for Feature
Selection in Unsupervised Learning
Rajalaxmi Ramasamy and Sylvia Rani
Department of Computer Science and Engineering, Kongu Engineering
College, India
Abstract: 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.
Keywords: Feature selection, unsupervised
learning, binary bat algorithm, mutation.