The Analysis of a Linear Classifier Developed through Particle Swarm Optimization

Authors : Fatih AYDIN

Pages : 36-49

Doi:10.53070/bbd.1259377

View : 61 | Download : 43

Publication Date : 2023-06-08

Article Type : Research Paper

Abstract :Meta-heuristics are high-level approaches developed to discover a heuristic that provides a reasonable solution to many varieties of optimization problems. The classification problems contain a sort of optimization problem. Simply, the objective herein is to reduce the number of misclassified instances. In this paper, the question of whether meta-heuristic methods can be used to construct linear models or not is answered. To this end, Particle Swarm Optimization insert ignore into journalissuearticles values(PSO); has been engaged to address linear classification problems. The Particle Swarm Classifier insert ignore into journalissuearticles values(PSC); with a certain objective function has been compared with Support Vector Machine insert ignore into journalissuearticles values(SVM);, Perceptron Learning Rule insert ignore into journalissuearticles values(PLR);, and Logistic Regression insert ignore into journalissuearticles values(LR); applied to fifteen data sets. The experimental results point out that PSC can compete with the other classifiers, and it turns out to be superior to other classifiers for some binary classification problems. Furthermore, the average classification accuracies of PSC, SVM, LR, and PLR are 80.8%, 80.6%, 80.9%, and 57.7%, respectively. In order to enhance the classification performance of PSC, more advanced objective functions can be developed. Further, the classification accuracy can be boosted more by constructing tighter constraints via another meta-heuristic.
Keywords : makine öğrenmesi, yapay zeka, parçacık sürü optimizasyonu, meta sezgisel algoritmalar, denetimli öğrenme

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