State Key Laboratory of Chemo/Biosensing and Chemometrics, College of Chemistry and Chemical Engineering, Hunan University, Changsha 410082, PR China.
Talanta. 2010 Mar 15;80(5):1698-701. doi: 10.1016/j.talanta.2009.10.009. Epub 2009 Oct 13.
Multivariate spectral analysis has been widely applied in chemistry and other fields. Spectral data consisting of measurements at hundreds and even thousands of analytical channels can now be obtained in a few seconds. It is widely accepted that before a multivariate regression model is built, a well-performed variable selection can be helpful to improve the predictive ability of the model. In this paper, the concept of traditional wavelength variable selection has been extended and the idea of variable weighting is incorporated into least-squares support vector machine (LS-SVM). A recently proposed global optimization method, particle swarm optimization (PSO) algorithm is used to search for the weights of variables and the hyper-parameters involved in LS-SVM optimizing the training of a calibration set and the prediction of an independent validation set. All the computation process of this method is automatic. Two real data sets are investigated and the results are compared those of PLS, uninformative variable elimination-PLS (UVE-PLS) and LS-SVM models to demonstrate the advantages of the proposed method.
多元光谱分析已经广泛应用于化学和其他领域。现在，在几秒钟内就可以获得由数百甚至数千个分析通道的测量值组成的光谱数据。人们普遍认为，在建立多元回归模型之前，进行良好的变量选择有助于提高模型的预测能力。本文扩展了传统波长变量选择的概念，并将变量加权的思想纳入到最小二乘支持向量机（LS-SVM）中。最近提出的全局优化方法，粒子群优化（PSO）算法，用于搜索变量的权重和 LS-SVM 中的超参数，以优化校准集的训练和独立验证集的预测。该方法的所有计算过程都是自动化的。研究了两个真实数据集，并将结果与 PLS、无信息变量消除-PLS（UVE-PLS）和 LS-SVM 模型进行比较，以证明该方法的优势。
