Random Machines: A bagged-weighted support vector model with free kernel choice

TL;DR

This paper introduces Random Machines, a novel ensemble framework that automates kernel selection for support vector machines, improving accuracy and reducing computational time on various datasets.

Contribution

The paper proposes a new ensemble method called Random Machines that automates kernel selection in SVMs, enhancing efficiency and performance.

Findings

Improved accuracy on benchmark datasets

Reduced computational time for kernel tuning

Effective kernel selection in SVMs

Abstract

Improvement of statistical learning models in order to increase efficiency in solving classification or regression problems is still a goal pursued by the scientific community. In this way, the support vector machine model is one of the most successful and powerful algorithms for those tasks. However, its performance depends directly from the choice of the kernel function and their hyperparameters. The traditional choice of them, actually, can be computationally expensive to do the kernel choice and the tuning processes. In this article, it is proposed a novel framework to deal with the kernel function selection called Random Machines. The results improved accuracy and reduced computational time. The data study was performed in simulated data and over 27 real benchmarking datasets.