Ensemble learning methods, such as Bagging and Boosting, are well-regarded for their ability to enhance model performance by combining diverse base learners. These approaches leverage the strengths of individual models to achieve more accurate and robust predictions. However, real-world datasets often contain noise, which can significantly impair model effectiveness. This paper focuses on two prevalent and challenging types: feature noise, which can lead to fitting instability and poor generalization, and label noise, which can lead to erroneous supervision and model overfitting. Recognizing the inherent properties of ensemble learning, particularly its focus on optimizing the decision margin to improve classification accuracy, we see an opportunity to bolster ensemble model robustness. To address both feature and label noise, we propose a novel approach called Dual Geometry Margin Boosting (DGMB). This method employs two key strategies: the Decision Plane Margin (DPM), which enhances class separation, and the Hyper-Sphere Margin (HSM), which effectively filters out potentially noisy samples during the learning process. Our experiments demonstrate the impressive ability of DGMB to resist both feature and label noise. Through rigorous testing on various noise-contaminated datasets, we show that DGMB maintains strong performance and outperforms other robust Ensemble methods.
Wang et al. (Thu,) studied this question.