Paper Info
Title
Selective Ensemble Modeling Approach based on Variable Importance of Projection With its Application<sup>1</sup>
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.
Year
DOI
Venue
2018
10.1109/ICInfA.2018.8812566
2018 IEEE International Conference on Information and Automation (ICIA)
Keywords
DocType
ISBN
Feature selection,variable importance of projection (VIP),selective ensemble (SEN) modeling,kernel latent structure algorithm,soft measuring model.
Conference
978-1-5386-8070-4
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Jian Tang1526148.30
Jun-Fei Qiao279874.56
Wen Yu324652.12