A bagging and importance sampling approach to Support Vector Machines

TL;DR

This paper introduces a combined bagging and importance sampling method to efficiently solve SVM problems on large datasets, aiming to reduce computation time while maintaining prediction accuracy.

Contribution

It proposes a novel approach that integrates importance sampling with bagging for faster SVM solutions, building on previous nearest neighbors techniques.

Findings

Faster SVM solutions on large datasets

Maintains comparable prediction accuracy

Effective in benchmark tests

Abstract

An importance sampling and bagging approach to solving the support vector machine (SVM) problem in the context of large databases is presented and evaluated. Our algorithm builds on the nearest neighbors ideas presented in Camelo at al. (2015). As in that reference, the goal of the present proposal is to achieve a faster solution of the SVM problem without a significance loss in the prediction error. The performance of the methodology is evaluated in benchmark examples and theoretical aspects of subsample methods are discussed.