An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data.
An Efficient Elastic Net with Regression Coefficients Method for Variable Selection of Spectrum Data.
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一种基于回归系数的高效弹性网络光谱数据变量选择方法
DOI：
10.1371/journal.pone.0171122
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Li Q
中科院分区：
文献类型：
--
作者：
Liu W;Li Q
利用光谱数据进行质量预测往往会受到噪声和共线性的影响，因此变量选择方法在光谱数据处理中起着重要的作用。本文提出了一种有效的弹性网络回归系数法（Enet-BETA）来选择光谱数据中的重要变量。提出的Enet-BETA方法不仅可以选择重要变量，使质量易于解释，而且可以提高所建模型的稳定性和可行性。由于弹性网络法减少了冗余变量，因此Enet-BETA法不易出现过拟合。假设检验用于进一步简化模型，并提供对过程性质的更好洞察。实验结果表明，所提出的Enet-BETA方法优于其他方法的预测性能和模型解释。
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.