Year: 2025
Type: preprint
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.
Source: arXiv (Cornell University)
Institutions: Huawei Technologies (France), Huawei Technologies (Sweden), Institut Polytechnique de Paris, Télécom SudParis, Chinese University of Hong Kong, Technology Innovation Institute, Chinese University of Hong Kong, Shenzhen, Fraunhofer Institute for Telecommunications, Heinrich Hertz Institute, Aalto University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415024498
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