Ensembled Support Vector Machines for Meta-Modeling

Abstract

In many practical engineering problems, function forms cannot be given explicitly in terms of decision variables, but the value of functions can be evaluated for given decision variables through some experiments such as structural analysis, fluid mechanic analysis and so on. These experiments are usually expensive. In such cases, therefore, meta-models are usually constructed on the basis of a less number of samples. Those meta-models are improved in sequence by adding a few samples at one time in order to obtain a good approximate model with as a less number of samples as possible. Support vector machines (SVM) can be effectively applied to meta-modeling. In practical implementation of SVMs, however, it is important to tune parameters of kernels appropriately. Usually, cross validation (CV) techniques are applied to this purpose. However, CV techniques require a lot of function evaluation, which is not realistic in many real engineering problems. This paper shows that applying ensembled support vector machines makes it possible to tune parameters automatically within reasonable computation time.