Hamming Distance based Binary PSO for Feature Selection and Classification from high dimensional Gene Expression Data

In this article, a Binary Particle Swarm Optimization (BPSO) algorithm is proposed incorporating hamming distance as a distance measure between particles for feature selection problem from high dimensional microarray gene expression data. Hamming distance is used as an similarity measurement for updating the velocities of each particles or solutions. It also helps to reduce extra parameter (i.e. Vmin) as needed in conventional BPSO during velocity updation. An initial fast pre-processing heuristic method is used for crude domain reduction from high dimension. Then the fitness function is suitably designed in multi objective framework for further reduction and soft tuning on the reduced features using BPSO. The performance of the proposed method is tested on three benchmark cancerous datasets (i.e., colon, lymphoma and leukemia cancer). The comparative study is also performed on the existing literature to show the effectiveness of the proposed method.