Particle Swarm Optimization (PSO)-based Feature Selection: An Approach for Improving Performance of Classification Model
Main Article Content
Abstract
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
Article Details
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