A rough-based robust support vector regression network for function approximation
Chih-Ching Hsiao, Shun‐Feng Su, Chen‐Chia Chuang · 2011
Support vector regression (SVR) employs the support vector machine (SVM) to tackle problems of function approximation and regression estimation. SVR has been shown to have good robust properties against noise. Besides, in SVR, outliers may also possibly be taken as support vectors. Such an inclusion of outliers in support vectors may lead to seriously overfitting phenomena. The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. In this paper, a novel regression approach, termed as the Rough Margin Support Vector Regression (RMSVR) network, is proposed to enhance the robust capability of SVR. The basic idea of the approach is to adopt the concept of rough sets to construct the model obtained by SVR and fine tune it with a robust learning algorithm. Simulation results of the proposed approach have shown the effectiveness of the approximated function in discriminating against outliers.