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In our recent work, Monte Carlo Cross Validation Stacked Regression (MCCVSR) is proposed to achieve automatic optimization of spectral interval selection in multivariate calibration. Though MCCVSR performs well in normal conditions, it is still necessary to improve it for more general applications. According to the well-known principle of "garbage in, garbage out (GIGO)", as a precise ensemble method, MCCVSR might be influenced by outlying and very bad submodels. In this paper, a statistical test is designed to exclude the ruinous submodels from the ensemble learning process, therefore, the combination process becomes more reliable. Though completely automated, the proposed method is adjustable according to the nature of the data analyzed, including the size of training samples, resolution of spectra and quantitative potentials of the submodels. The effectiveness of the submodel refining is demonstrated by the investigation of a real standard data.
Journal of automated methods & management in chemistry. 2009:2009:291820. doi: 10.1155/2009/291820 0.02024
An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration
一种完全自动化的谱区选择的新型集成方法及其在多元校正中的应用研究 翻译改进
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DOI: 10.1155/2009/291820 PMID: 19547705
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