A robust support vector regression with exact predictors and fuzzy responses

Abstract

In this paper, a new method is proposed for estimating fuzzy regression models based on a novel robust support vector machines with exact predictors and fuzzy responses. For this purpose, a three-stage support vector machine algorithm was introduced based on a modified robust loss function. Some common goodness-of-fit criteria and a popular kernel were also employed to examine the performance of the proposed method in cases where the outliers occur in the data set. The effectiveness of the proposed method was illustrated through three numerical cases including a simulation study and two applied examples. The proposed method was also compared with several common fuzzy linear/nonlinear/nonparametric regression models. The numerical results clearly indicated that the proposed model is capable of providing accurate results in the cases involving data sets with or without outliers. Thus, the proposed fuzzy regression model can be successfully applied to improve the prediction accuracy and interpretability of the fuzzy regression models for real-life applications in the intelligence systems.