Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection

Abstract

This paper presents new data mining-based multivariate calibration models for predicting internal bond strength from medium density fiberboard (MDF) process variables. It utilizes genetic algorithms (GA) based variable selection combined with several calibration methods. By adopting a proper variable selection scheme, the prediction performance can be… (More)
DOI: 10.1007/s00226-008-0204-7

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Cite this paper

@article{Andr2008PredictionOI, title={Prediction of internal bond strength in a medium density fiberboard process using multivariate statistical methods and variable selection}, author={Nicolas Andr{\'e} and Hyun-Woo Cho and Seung Hyun Baek and Myong-Kee Jeong and Timothy M. Young}, journal={Wood Science and Technology}, year={2008}, volume={42}, pages={521-534} }