Capped Linex Metric Twin Support Vector Machine for Robust Classification

In this paper, a novel robust loss function is designed, namely, capped linear loss function Simultaneously, we give some ideal and important properties of L-a epsilon, such as boundedness, non-convexity and robustness. Furthermore, a new binary classification learning method is proposed via introducing L-a epsilon, which is called the robust twin support vector machine (Linex-TSVM). Linex-TSVM can not only reduce the influence of outliers on Linex-SVM, but also improve the classification performance and robustness of Linex-SVM. Moreover, the effect of outliers on the model can be greatly reduced by introducing two regularization terms to realize the structural risk minimization principle. Finally, a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem Linex-TSVM, and the time complexity of the algorithm is analyzed, which proves that the model satisfies the Bayes rule. Experimental results on multiple datasets demonstrate that the proposed Linex-TSVM can compete with the existing methods in terms of robustness and feasibility.

Automated summary

In this paper, a novel robust loss function is designed and a new binary classification learning method is proposed to improve classification performance and robustness while reducing the influence of outliers on the model. The introduction of regularization terms realizes the principle of structural risk minimization, and a simple and efficient iterative algorithm is designed to solve the non-convex optimization problem.