کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1180996 | 962888 | 2011 | 8 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: A two-stage regression approach for spectroscopic quantitative analysis A two-stage regression approach for spectroscopic quantitative analysis](/preview/png/1180996.png)
In this paper, we propose a two-stage regression approach, which is based on the residual correction concept. Its underlying idea is to correct any given regressor by analyzing and modeling its residual errors in the input space. We report and discuss results of experiments conducted on three different datasets in infrared spectroscopy and designed in such a way to test the proposed approach by: 1) varying the kind of adopted regression method used to approximate the chemical parameter of interest. Partial least squares regression (PLSR), support vector machines (SVM) and radial basis function neural network (RBF) methods are considered; 2) adopting or not a feature selection strategy to reduce the dimension of the space where to perform the regression task. A comparative study with another approach which exploits differently estimation errors, namely adaptive boosting for regression (AdaBoost.R), is also included. The obtained results point out that the residual-based correction approach (RBC) can improve the accuracy of the estimation process. Not all the improvements are statistically significant but, at the same time, no case of accuracy decrease has been observed.
► An approach to correct any given regressor is proposed.
► A comparative study with adaptive boosting is included.
► Experiments show estimation accuracy can be improved.
Journal: Chemometrics and Intelligent Laboratory Systems - Volume 109, Issue 1, 15 November 2011, Pages 34–41