Monte Carlo cross validation

By Qingsong Xu, Yizeng Liang. 2001

972 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4d1384443ad08ce39ba7f6d18a87f1b9a6bb95de

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In Analytical Chemistry

In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

Machine learning–XGBoost analysis of language networks to classify patients with epilepsy (2017, methodology): “1, we have used a nested crossvalidation scheme with an outer Monte Carlo cross-validation (MCCV) (see [46, 47]), also called random subsampling, repeated twelve times in order to reduce variance and an inner k-fold cross-validation (k-fold CV) for feature selection with k = 5 (see [44, 45]).”
Cross-validation: what does it estimate and how well does it do it? (2021, background): “…models, such as when selecting a model or a good value of a learning algorithm’s hyperparameters (e.g., Stoica et al., 1986; Shao, 1993; Zhang, 1993; Dietterich, 1998; Xu and Liang, 2001; Yang, 2007; Arlot and Celisse, 2010; Fong and Holmes, 2020; Sivula et al., 2020; Riley et al., 2021).”
Multiblock partial least squares regression based on wavelet transform for quantitative analysis of near infrared spectra (2010, methodology): “(7), PRESSi = 1 N ∑ N j=1 ∑ M k=1 ðyjk− ŷjkÞ2 ! ð7Þ where N is the model number involved in the MCCV, and M is the sample number of the test set.”
Determination of optimum number of components in partial least squares regression from distributions of the root‐mean‐squared error obtained by Monte Carlo resampling (2018, methodology): “Monte Carlo resampling was introduced to the chemometric community 15 to 20 years ago.(4,5) Resampling repeatedly splits the data into a calibration set and a validation set with the aim to select the model with optimum number of components.”
Identification of grapevine varieties using leaf spectroscopy and partial least squares (2013, methodology): “capability of providing accurate results for spectra that were not employed in its creation), Monte-Carlo cross-validation (MCCV) (Picard and Cook, 1984; Xu and Liang, 2001; Arlot, 2010) was performed.”
A randomization test for PLS component selection (2007, methodology): “study indicate that the method may outperform ‘conventional’ implementations for small and medium-sized calibration sets [35].”
Stacked partial least squares regression analysis for spectral calibration and prediction (2009, methodology): “In Monte Carlo cross-validation stacked regression (MCCVSR) modeling reported by Xu [15], fixedwindow subsets of the response spectrum are stacked to try to overcome the disadvantages of wavelength selection inherent in iPLS and MWPLS modeling.”
Bootstrap Cross-Validation Indices for PLS Path Model Assessment (2010, methodology): “Such an approach has been applied in chemometrics to determine the number of components in a PLS models (Du et al. 2006; Xu et al. 2004; Xu and Liang 2001 ).”