Authors: Sonu Lal Gupta; Anurag Singh Baghel
Addresses: SICT, Gautam Buddha University, India ' SICT, Gautam Buddha University, India
Abstract: Feature selection is an important process in text classification. In general, traditional feature selection approaches are based on exhaustive search hence become inefficient due to a large search space. Further, this task becomes more challenging as the number of features increases. Recently, evolutionary computation (EC)-based search techniques have received a lot of attention in solving feature selection problem in high-dimensional feature space. This paper proposes a particle swarm optimisation (PSO)-based feature selection approach which is capable of generating the desired number of high-quality features from a large feature space. The proposed algorithm is tested on a large dataset and compared with several existing state-of-the-art algorithms used for feature selection. The accuracy of the underlying classifier has been considered as a measure of performance. Our obtained results demonstrated that the proposed PSO-based feature selection approach outperforms the other traditional feature selection algorithms in all the considered classifiers.
Keywords: sentiment classification; feature selection; particle swarm optimisation; PSO; evolutionary computation; support vector machine; SVM; naïve Bayes; NB; mutual information; MI; chi-square; CHI.
International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.4, pp.526 - 541
Received: 11 Jul 2017
Accepted: 30 Mar 2018
Published online: 28 Apr 2020 *