Institute of Automation, Chinese Academy of Sciences, 100190 Beijing, China.
Anal Chim Acta. 2013 Aug 20;792:19-27. doi: 10.1016/j.aca.2013.07.008. Epub 2013 Jul 9.
A new ensemble learning algorithm is presented for quantitative analysis of near-infrared spectra. The algorithm contains two steps of stacked regression and Partial Least Squares (PLS), termed Dual Stacked Partial Least Squares (DSPLS) algorithm. First, several sub-models were generated from the whole calibration set. The inner-stack step was implemented on sub-intervals of the spectrum. Then the outer-stack step was used to combine these sub-models. Several combination rules of the outer-stack step were analyzed for the proposed DSPLS algorithm. In addition, a novel selective weighting rule was also involved to select a subset of all available sub-models. Experiments on two public near-infrared datasets demonstrate that the proposed DSPLS with selective weighting rule provided superior prediction performance and outperformed the conventional PLS algorithm. Compared with the single model, the new ensemble model can provide more robust prediction result and can be considered an alternative choice for quantitative analytical applications.
提出了一种新的集成学习算法，用于近红外光谱的定量分析。该算法包含堆叠回归和偏最小二乘（PLS）的两个步骤，称为双堆叠偏最小二乘（DSPLS）算法。首先，从整个校准集中生成了几个子模型。内堆叠步骤在光谱的子区间上执行。然后，外堆叠步骤用于组合这些子模型。针对所提出的 DSPLS 算法，分析了外堆叠步骤的几种组合规则。此外，还涉及一种新的选择性加权规则，用于从所有可用子模型中选择子集。在两个公开的近红外数据集上的实验表明，具有选择性加权规则的所提出的 DSPLS 提供了优越的预测性能，并优于传统的 PLS 算法。与单个模型相比，新的集成模型可以提供更稳健的预测结果，可被视为定量分析应用的另一种选择。
