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An Improved Ensemble Method for Completely Automatic Optimization of Spectral Interval Selection in Multivariate Calibration
2009
Journal of automated methods & management in chemistry (Print)
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
doi:10.1155/2009/291820
pmid:19547705
pmcid:PMC2696029
fatcat:tpzwbzefxvcpxckuphnqdrqfgq