High-Dimensional Analysis of Bootstrap Ensemble Classifiers

TL;DR

This paper provides a theoretical analysis of bootstrap ensemble classifiers, specifically LSSVM, in high-dimensional settings, using Random Matrix Theory to guide parameter selection and validate with experiments.

Contribution

It offers new theoretical insights into bootstrap methods for high-dimensional LSSVM ensembles and proposes strategies for optimal parameter choices.

Findings

Bootstrap methods improve LSSVM performance in high dimensions.

Theoretical guidelines for selecting number of subsets and regularization.

Empirical validation confirms theoretical predictions.

Abstract

Bootstrap methods have long been the cornerstone of ensemble learning in machine learning. This paper presents a theoretical analysis of bootstrap techniques applied to the Least Square Support Vector Machine (LSSVM) ensemble in the context of large and growing sample sizes and feature dimensionalities. Using tools from Random Matrix Theory, we investigate the performance of this classifier that aggregates decision functions from multiple weak classifiers, each trained on different subsets of the data. We provide insights into the use of bootstrap methods in high-dimensional settings, enhancing our understanding of their impact. Based on these findings, we propose strategies to select the number of subsets and the regularization parameter that maximize the performance of the LSSVM. Empirical experiments on synthetic and real-world datasets validate our theoretical results.