Unsupervised feature selection using binary bat algorithm

Feature selection is selecting a subset of optimal features. Feature selection is being used in high dimensional data reduction and it is being used in several applications like medical, image processing, text mining, etc. Several methods were introduced for unsupervised feature selection. Among those methods some are based on filter approach and some are based on wrapper approach. In the existing work, unsupervised feature selection methods using Genetic Algorithm, Particle Swarm Optimization with Relative Reduct, Quick Reduct and Ant Colony Optimization have been introduced. These methods yield better performance for unsupervised feature selection. In this paper we proposed a novel method to select subset of features from unlabeled data using binary bat algorithm with sum of squared error as the fitness function. The proposed method is then tested with various classification algorithms like decision tree, multilayer perceptron, support vector machine and clustering quality measures like sum of squared error. The results show that our proposed method gives more accuracy when compared with other optimization algorithm.