Selective Ensemble Modeling Approach based on Variable Importance of Projection With its Application1

Abstract

In industrial process, data-driven soft measuring model based on easy-to-measure process variables can be used to inference and estimate difficulty-to-measure quality or quantity parameter effectively. Normally, there is strong collinearity among these input features. Moreover, only small-size useful input/output data pairs for modeling such difficulty-to-measure predicted parameters can be obtained. In this paper, a new selective ensemble (SEN) modeling approach based on variable importance of projection (VIP) index is proposed to address such data. The VIP values of different input features combined with prior knowledge is used to make feature selection. These selected features are used to construct soft measuring model based on Resample training sample ensemble construction strategy and SEN kernel latent structure algorithm. Simulation results based on mechanical frequency and Near-infrared (NIR) spectral data show effectiveness of the proposed method.