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Robust boosting algorithms have emerged as alternative solutions totraditional boosting techniques for addressing label noise in classificationtasks. However, these methods have predominantly focused on binaryclassification, limiting their applicability to multi-class tasks. Furthermore,they encounter challenges with imbalanced datasets, missing values, andcomputational efficiency. In this paper, we establish that the loss functionemployed in advanced Gradient Boosting Decision Trees (GBDT), particularlyNewton's method-based GBDT, need not necessarily exhibit global convexity.Instead, the loss function only requires convexity within a specific region.Consequently, these GBDT models can leverage the benefits of nonconvex robustloss functions, making them resilient to noise. Building upon this theoreticalinsight, we introduce a new noise-robust boosting model called Robust-GBDT,which seamlessly integrates the advanced GBDT framework with robust losses.Additionally, we enhance the existing robust loss functions and introduce anovel robust loss function, Robust Focal Loss, designed to address classimbalance. As a result, Robust-GBDT generates more accurate predictions,significantly enhancing its generalization capabilities, especially inscenarios marked by label noise and class imbalance. Furthermore, Robust-GBDTis user-friendly and can easily integrate existing open-source code, enablingit to effectively handle complex datasets while improving computationalefficiency. Numerous experiments confirm the superiority of Robust-GBDT overother noise-robust methods.

Robust-GBDT: A Novel Gradient Boosting Model for Noise-Robust Classification