Feature Selection using a Novel Particle Swarm Optimization and It’s Variants

Feature selection has been keen area of research in classification problem. Most of the researchers mainly concentrate on statistical measures to select the feature subset. These methods do not provide a suitable solution because the search space increases with the feature size. The FS is a very popular area for applications of populationbased random techniques. This paper suggests swarm optimization technique, binary particle swarm optimization technique and its variants, to select the optimal feature subset. The main task of the BPSO is the selection of the features used by the SVM in the classification of spambase data set. The results of our experiments show a very strong relation between number of features and accuracy. Comparison of the optimized results and the unoptimized results showed that the BPSO-MS method could significantly reduce the computation cost while improving the classification accuracy.