Hybrid particle swarm optimization algorithm for text feature selection problems

• Published in: Neural computing & applications 2024-05, Vol.36 (13), p.7471-7489
• Main Authors: Nachaoui, Mourad, Lakouam, Issam, Hafidi, Imad
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Clustering; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Constrictions; Data Mining and Knowledge Discovery; Feature selection; Genetic algorithms; Heuristic methods; Image Processing and Computer Vision; Inertia; Original Article; Particle swarm optimization; Probability and Statistics in Computer Science; Swarm intelligence; Topology
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-024-09472-w

Feature selection (FS) is a crucial preprocessing step that aims to eliminate irrelevant and redundant features, reduce the dimensionality of the feature space, and enhance clustering efficiency and effectiveness. FS is categorized as NP-Hard due to the high number of existing solutions. Various metaheuristic methods have been developed to address the FS problem, yielding promising results. Particularly, particle swarm optimization (PSO), an evolutionary computing (EC) approach guided by swarm intelligence, has gained widespread adoption owing to its implementation simplicity and potential for global search. This paper analyzes several variants of PSO algorithms and introduces a new FS method called HPSO. The proposed approach utilizes an asynchronously adaptive inertia weight and an improved constriction factor. Additionally, it incorporates a chaotic map and a MAD fitness function with a feature count penalty to tackle the clustering FS problem. The efficiency of the developed method is evaluated against the genetic algorithm (GA) and well-known variants of PSO algorithms, including PSOs with fixed inertia weights, PSOs with improved inertia weights, PSOs with fixed constriction factors, PSOs with improved constriction factors, PSOs with adaptive inertia weights, and PSO’s includes advanced learning exemplars and sophisticated structure topologies. This paper assesses two different reference text data sets, Reuters-21578 and Webkb. In comparison with competitive methods, the proposed HPSO method achieves higher clustering precision and selects a more informative feature set.