Dimensionality reduction using GA-PSO

Abstract

The feature selection process can be considered a problem of global combinatorial optimization in machine learning, which reduces the number of features, removes irrelevant, noisy and redundant data, and results in acceptable classification accuracy. In this paper, we propose a combination of genetic algorithms (GAs) and particle swarm optimization (PSO) for feature selection. The K-nearest neighbor (K-NN) method with leave-one-out cross-validation (LOOCV) serves as an evaluator for the GAs and the PSO. The proposed method is applied to five classification problems taken from the literature. Experimental results show that our method simplifies features effectively and obtains a higher classification accuracy compared to other feature selection methods.