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.
| Subjects: | Machine Learning (stat.ML); Machine Learning (cs.LG) |
| Cite as: | arXiv:2505.14587 [stat.ML] |
| (or arXiv:2505.14587v2 [stat.ML] for this version) | |
| https://doi.org/10.48550/arXiv.2505.14587 arXiv-issued DOI via DataCite |
Submission history
From: Hamza Cherkaoui PhD [view email]
[v1]
Tue, 20 May 2025 16:40:43 UTC (1,913 KB)
[v2]
Wed, 13 May 2026 07:56:00 UTC (204 KB)