Pointwise ensemble of meta-models using v nearest points cross-validation

As the use of meta-models to replace computationally-intensive simulations for estimating real system behaviors increases, there is an increasing need to select appropriate meta-models that well represent real system behaviors. Since in most cases designers do not know the behavior of the real system a priori, however, they often have trouble selecting a suitable meta-model. In order to provide robust prediction performance, ensembles of meta-models have been developed which linearly combines stand-alone meta-models. In this study, we propose a new pointwise ensemble of meta-models whose weights vary according to the prediction point of interest. The suggested method can include all kinds of stand-alone meta-models for ensemble construction, and can interpolate real system response values at training points, even if regression models are included as stand-alone meta-models. To evaluate the effectiveness of the proposed method, its prediction performance is compared with those of existing ensembles of meta-models using well-known mathematical functions. The results show that our pointwise ensemble of meta-models provides more robust and accurate predictions than existing models for a majority of test problems.