Particle Swarm Optimization (PSO)-based Feature Selection: An Approach for Improving Performance of Classification Model - OYEDEMI, OLUYEMISI; Awoyelu, Iyabo Olukemi; Adagunodo, Emmanuel Rotimi (INFOCOMP Journal of Computer Science, 2025)
The Particle Swarm Optimization (PSO) algorithm was implemented in this paper for feature selection in order to improve the performance of web phishing classification model. The algorithm initialized a population of particles, each of which represented a possible feature subset, and then iteratively investigated subsets of features. The particles are updated according to their global and personal best locations in an effort to choose the best feature subset. A total of 36 features were selected out of the downloaded dataset comprising of 48 features. The performance of the whole dataset was measured against the performance of the selected dataset. It was observed that the performance of the classifier improved across all the metrics. The accuracy, precision, recall and F1-score increased from 93% to 96%, 92% to 97%, 92% to 96% and 93% to 97% respectively. The results indicated that the PSO algorithm effectively identified a subset of features that improved the performance of the classifier. The PSO algorithm’s optimal feature subset produces superior classification performance when compared to using the whole dataset. Through feature selection, this method demonstrated how well PSO works to improve classifier accuracy and efficiency across a range of classification tasks. Keywords: Classifier Performance, Feature Selection, Machine Learning, Particle Swarm Optimization (PSO), Optimization, Redundancy