Feature selection is the main step in classification systems, a procedure that selects a
subset from original features. Feature selection is one of major challenges in text categorization.
The high dimensionality of feature space increases the complexity of text
categorization process, because it plays a key role in this process. This paper presents a
novel feature selection method based on particle swarm optimization to improve the performance
of text categorization. Particle swarm optimization inspired by social behavior
of fish schooling or bird flocking. The complexity of the proposed method is very low
due to application of a simple classifier. The performance of the proposed method is compared
with performance of other methods on the Reuters-21578 data set. Experimental
results display the superiority of the proposed method.