Open AccessUsage of Particle Swarm Optimization to Improve the Performance of Supervised Classifiers
Author(s) -
N. Bharanidharan,
M. Nivetha,
Ashwin Balakrishna,
P. Dhanaraju
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1084/1/012033
Subject(s) - particle swarm optimization , classifier (uml) , artificial intelligence , computer science , gradient descent , pattern recognition (psychology) , feature selection , linear discriminant analysis , multi swarm optimization , metaheuristic , feature vector , linear classifier , machine learning , artificial neural network
Representing the data appropriately will have a significant effect on the outcome produced by the classifier. Transforming the feature will help to represent the data points in a more suitable way for the classifier. Particle swarm optimization belongs to the Swarm Optimization techniques category and it is generally used for solving numerical optimization problems, weight updation in neural networks, and feature selection. This research work proposes a Particle swarm optimization-based transformation technique for increasing the classification metrics of popular classification algorithms namely K-Nearest Neighbor, Stochastic Gradient Descent Classifier, Decision trees, and linear discriminant analysis classifier. Experiments are conducted using the SONAR dataset and the highest accuracy of 82.69% is attained for Stochastic Gradient Descent classifier when Particle swarm optimization is used as a transformation technique