A reduced support vector machine approach for interval regression analysis

The support vector machine (SVM) has been shown to be an efficient approach for a variety of classification problems. It has also been widely used in pattern recognition, regression and distribution estimation for separable data. However, there are two problems with using the SVM model: (1) Large-scale: when dealing with large-scale data sets, the solution may be difficult to find when using SVM with nonlinear kernels; (2) Unbalance: the number of samples from one class is much larger than the number of samples from the other classes. It causes the excursion of separation margin. Under these circumstances, developing an efficient method is necessary. Recently, the use of the reduced support vector machine (RSVM) was proposed as an alternative to the standard SVM. It has been proven more efficient than the traditional SVM in processing large-scaled data. In this paper, we introduce the principle of RSVM to evaluate interval regression analysis. The main idea of the proposed method is to reduce the number of support vectors by randomly selecting a subset of samples.