Improved PSO-based feature construction algorithm using Feature Selection Methods

Feature construction (FC) can improve the classification performance by creating powerful features from the original ones. Particle Swarm Optimization (PSO) is a global search technique that can construct features directly. We believe that using raw features may lead the PSO-based FC method to an inefficient feature, so in this paper, the aim is to select the prominent features before applying PSO-based FC method. The Forward Feature Selection (FFS) method is used for selecting more informative feature subset from original set and constructing feature by the selected ones. Experimental results show that the proposed method can increase the accuracy by constructing a new powerful feature.