Text Feature Selection using Particle Swarm Optimization Algorithm

Abstract

Abstract: Text Categorization (TC) has become recently an important technology in the field of organizing a huge number of documents. Feature Selection (FS) is commonly used to reduce dimensionality of text datasets with huge number of features which would be difficult to process further. In this paper we have implemented an efficient feature selection algorithm based on Particle Swarm Optimization (PSO) to improve the performance of Arabic text categorization. PSO is a search algorithm that employs a population of particles existing within a multi-dimensional space. We have used Radial Basis Function (RBF) networks as a text classifier. The performance of the proposed algorithm is compared to the performance of document frequency, tf×idf and Chi-square statistic algorithms. Simulation results on the Arabic dataset show the superiority of the proposed algorithm. Key words: Text categorization • feature selection • particle swarm optimization • radial basis function networks • wrapper feature selection method