A Unified Feature Selection Model for High Dimensional Clinical Data Using Mutated Binary Particle Swarm Optimization and Genetic Algorithm

This article contends that feature selection is an important pre-processing step in case the data set is huge in size with many features. Once there are many features, then the probability of existence of noisy features is high which might bring down the efficiency of classifiers created out of that. Since the clinical data sets naturally having very large number of features, the necessity of reducing the features is imminent to get good classifier accuracy. Nowadays, there has been an increase in the use of evolutionary algorithms in optimization in feature selection methods due to the high success rate. A hybrid algorithm which uses a modified binary particle swarm optimization called mutated binary particle swarm optimization and binary genetic algorithm is proposed in this article which enhanced the exploration and exploitation capability and it has been a verified with proposed parameter called trade off factor through which the proposed method is compared with other methods and the result shows the improved efficiency of the proposed method over other methods.