An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data.
Using the spectrum data for quality prediction always suffers from noise and colinearity, so variable selection method plays an important role to deal with spectrum data. An efficient elastic net with regression coefficients method (Enet-BETA) is proposed to select the significant variables of the spectrum data in this paper. The proposed Enet-BETA method can not only select important variables to make the quality easy to interpret, but also can improve the stability and feasibility of the built model. Enet-BETA method is not prone to overfitting because of the reduction of redundant variables realized by elastic net method. Hypothesis testing is used to further simplify the model and provide a better insight into the nature of process. The experimental results prove that the proposed Enet-BETA method outperforms the other methods in terms of prediction performance and model interpretation.
School of Control Science and Engineering, Dalian University of Technology, Dalian, China.
Wenya
W
Liu
School of Control Science and Engineering, Dalian University of Technology, Dalian, China.
Qi
Q
Li
D000465:Algorithms; D001499:Bayes Theorem; D003460:Crystallization; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D010940:Plant Proteins / Q000032:analysis; D025341:Principal Component Analysis; D012044:Regression Analysis; D019265:Spectroscopy, Near-Infrared / Q000706:statistics & numerical data; D013057:Spectrum Analysis / Q000706:statistics & numerical data*; D014908:Triticum / Q000737:chemistry
D010940:Plant Proteins