Generalized Adaptive Huber Loss Driven Robust Twin Support Vector Machine Learning Framework for Pattern Classification

Aiming at the problem that the traditional Twin Support Vector Machine (TSVM) is sensitive to noise and outliers, this paper proposes a twin support vector machine model based on generalized adaptive Huber loss function (GAHTSVM). Via dynamically adjusting the robust parameters $$s$$ , the model can effectively suppress the adverse effects of noisy data points on the decision function, and improve the sparsity of the model by introducing insensitive region design. For non-convex optimization problems, concave-convex process (CCCP) is adopted to avoid quadratic programming problems and significantly improve the efficiency of the algorithm. Experiments show that GAHTSVM performs well in the scenarios where Gaussian noise is added to real-world datasets and outliers are introduced to artificial datasets: it is significantly better than other algorithms in low noise environment; In the high noise environment, it still maintains a leading position, and most datasets are ranked in the top; It performs well on two artificial datasets and is significantly superior to other algorithms.In conclusion, the model effectively overcomes the noise and outlier interference by dynamically adjusting the outlier penalty, and provides an efficient solution to the pattern recognition problem in high noise environment.