Abstract
We introduce a regression method that fully exploits both global and local information about a set of points in search of a suitable function explaining their mutual relationships. The points are assumed to form a repository of information granules. At a global level, statistical methods discriminate between regular points and outliers. Then the local component of the information embedded in the former is used to draw an optimal regression curve. We address the challenge of using a variety of standard machine learning tools such as support vector machine (SVM) or slight variants of them within the unifying hat of Granular Computing realm to obtain a definitely new featured nonlinear regression method. The performance of the proposed approach is illustrated with the aid of three well-known benchmarks and ad hoc featured datasets.