Optimization of Support Vector Machine using Firework Algorithm for Breast Cancer Classification
Year: 2024
Type: conference-paper
Abstract: The goal of this work is to enhance the realization and performance of Support Vector Machine (SVM) models by optimizing their hyperparameters using the Fireworks Algorithm (FWA). This paper addresses testing data from the Breast Cancer Wisconsin (Diagnostic) dataset compared with classical SVM models using models optimized with FWA. The FWA-optimized SVM gets much higher classification accuracy than the basic SVM, with the adjustment of the basic parameters optimum for the regularization constant, C, and the parameters of the kernel. Such results indicate the applicability of FWA in enhancing the performance of SVM and unveil its potential for hyperparameter optimization in different fields.
Authors: Kavish Panchal, Khushal Khaitan, Kiruthika M, A.Sheryl Oliver
Institution: SRM Institute of Science and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413911486