Robust Ensemble Learning Under Label Noise: A Theoretical Analysis and Framework-Specific Solutions.

Abstract

Ensemble learning methods combine multiple weak base learners to create a robust decision model, effectively analyzing feature-response relationships across various domains. However, the assumption of accurate sample-label relationships in supervised learning often breaks down in real-world datasets, leading to performance degradation due to incorrect information. The framework-specific effects of label noise on ensemble learning have not been sufficiently explored from a theoretical perspective. This article investigates the problem of learning from datasets contaminated by label noise within ensemble frameworks. We utilize bias-variance-diversity (BVD) decomposition theory to examine the impact of noisy labels on three mainstream ensemble paradigms: Bagging, Boosting, and Stacking. Our theoretical analysis characterizes the mechanisms behind performance degradation and guides the development of targeted strategies: data filtering (DF) for Bagging, sample reweighting (SR) for Boosting, and interactive feature purification (IFPS) for stacking. We validate our approaches on synthetic and real-world noisy-label benchmarks, demonstrating consistent improvements over traditional ensemble methods through extensive comparison and ablation experiments. Our findings offer actionable insights for enhancing the robustness of ensemble learning in the presence of noisy labels, thereby broadening its applicability in practical scenarios.