Feature Selection Empowered by Self-Inertia Weight Adaptive Particle Swarm Optimization for Text Classification

Text classification (TC) is a crucial practice in case of organizing a vast number of documents. The computational complexity of the TC process is usually high because of the large dimensionality of the feature space. Feature Selection (FS) procedures are used to extract the helpful information from the feature space and results in dimensionality reduction. The development of the FS method that reduces the dimensionality of feature space without compromising the categorization accuracy is desirable. This paper proposes a Self-Inertia Weight Adaptive Particle Swarm Optimization (SIW-APSO) based FS methodology to enhance the performance of text classification systems. SIW-APSO has fast convergence phenomena due to its high search competency and ability to find feature sub-set efficiently. For text classification, the K-nearest neighbors algorithm is used. The experimental analysis shows that the proposed method outperformed the existing state-of-the-art algorithms on the Reuters-21578 data set by achieving 98.60% precision, 96.56% recall, and 97.57% F1 score.