Open AccessAn Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data
Author(s) -
Wenya Liu,
Qi Li
Publication year - 2017
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0171122
Subject(s) - overfitting , elastic net regularization , feature selection , computer science , linear regression , regression , variable (mathematics) , quality (philosophy) , selection (genetic algorithm) , spectrum (functional analysis) , mathematics , data mining , artificial intelligence , statistics , machine learning , artificial neural network , physics , mathematical analysis , quantum mechanics
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.