Robust boosting algorithms have emerged as alternative solutions to traditional boosting techniques for addressing label noise in classification tasks. However, these methods have predominantly focused on binary classification, limiting their applicability to multi-class tasks. Furthermore, they encounter challenges with imbalanced datasets, missing values, and computational efficiency. In this paper, we establish that the loss function employed in advanced Gradient Boosting Decision Trees (GBDT), particularly Newton'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 robust loss functions, making them resilient to noise. Building upon this theoretical insight, 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 a novel robust loss function, Robust Focal Loss, designed to address class imbalance. As a result, Robust-GBDT generates more accurate predictions, significantly enhancing its generalization capabilities, especially in scenarios marked by label noise and class imbalance. Furthermore, Robust-GBDT is user-friendly and can easily integrate existing open-source code, enabling it to effectively handle complex datasets while improving computational efficiency. Numerous experiments confirm the superiority of Robust-GBDT over other noise-robust methods.

This paper presents a novel version of the hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional reduction technique to the feature matrices of the image datasets in order to reduce the "noise" and the redundant features in the feature matrices of the image datasets and to reduce the runtime constructing the hypergraph of the hypergraph neural network method. Then, the classic graph-based semi-supervised learning method, the classic hypergraph based semi-supervised learning method, the graph neural network, the hypergraph neural network, and our proposed hypergraph neural network are employed to solve the noisy label learning problem. The accuracies of these five methods are evaluated and compared. Experimental results show that the hypergraph neural network methods achieve the best performance when the noise level increases. Moreover, the hypergraph neural network methods are at least as good as the graph neural network.