A novel fusion Support Vector Machine integrating weak and sphere models for classification challenges with massive data

Abstract

The unprecedented growth in data generation has necessitated the adoption of advanced analytical techniques. Support Vector Machine (SVM) is a powerful machine learning tool that has proven invaluable in classifying observations through optimal hyperplane in higher dimensions. Despite their widespread use, SVM models encounter substantial challenges during the learning phase with massive datasets, necessitating strategic modifications. This paper introduces a novel fusion methodology incorporating weak and sphere support vector machine to address classification challenges with massive datasets. Comparative analyses across diverse simulated and benchmark real datasets underscore the efficacy of the proposed methodologies, exhibiting sustained predictive performance. The remarkable efficiency gain is noteworthy, as the learning phase requires only 10% of the computation time compared to conventional SVM approaches.