A New Meta-heuristic Bat Inspired Classification Approach for Microarray Data

Abstract

The main objective of a classifier is to discover the hidden class level of the unknown data. It is observed that data size, number of classes and dimension of feature space and inter class separability affect the performance of any classifier. For a long time, efforts are made in improving efficiency, accuracy and reliability of classifiers for a wide range of applications. Different optimization algorithms such as Particle Swarm Optimization (PSO) and Simulated Annealing (SA) have been used to enhance the accuracy of classifiers. Bat is also a metaheuristic search algorithm which is use to solve multi objective engineering problem. In this paper, a model has been proposed for classification using bat algorithm to update the weights of a Functional Link Artificial Neural Network (FLANN) classifier. Bat algorithm is based on the echolocation behaviour of bats. The proposed model has been compared with FLANN, PSO-FLANN. Simulation shows that the proposed classification technique is superior and faster than FLANN and PSO-FLANN.