Cross-validation for selecting a model selection procedure

By Yongli Zhang, Yuhong Yang. 2015

388 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

Multiple predicting K-fold cross-validation for model selection (2018, methodology): “This holds for our proposed algorithms, while Zhang and Yang (2015) revealed that the sample size in the validation set should be dominating under the high-dimensional model for the best procedure to be selected.”
A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking (2017, methodology): “One of the techniques to survey how a model can generalize to an independent data set is 10CV (Zhang and Yang, 2015; Bahrami et al., 2016).”
Model Selection Techniques: An Overview (2018, methodology): “For regression variable selection, it has been proved that the CV method is consistent in choosing between AIC and BIC given n t → ∞ , n v /n t → ∞ , and some other regularity assumptions [87, Thm. 1].”
Stacked Generalization: An Introduction to Super Learning (2017, neutral): “Though common, this number will not optimize performance in all settings [25].”
Analysis of k-Fold Cross-Validation over Hold-Out Validation on Colossal Datasets for Quality Classification (2016, background): “Thus, crossvalidation is widely accepted in data mining and machine learning community, and serves as a standard procedure for the sake of model selection or modeling procedure selection [4], [5].”
Cross-Validation for Correlated Data (2019, background): “A comprehensive comparison of CV variants in terms of prediction error estimation and models selection can be found in Zhang and Yang (2015).”