Name
Title | ||
|---|---|---|
Sensitivity analysis with cross-validation for feature selection and manifold learning |
Abstract | ||
|---|---|---|
The performance of a learning algorithm is usually measured in terms of prediction error. It is important to choose an appropriate estimator of the prediction error. This paper analyzes the statistical properties of the K-fold cross-validation prediction error estimator. It investigates how to compare two algorithms statistically. It also analyzes the sensitivity to the changes in the training/test set. Our main contribution is to experimentally study the statistical property of repeated cross-validation to stabilize the prediction error estimation, and thus to reduce the variance of the prediction error estimator. Our simulation results provide an empirical evidence to this conclusion. The experimental study has been performed on PAL dataset for age estimation task. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31346-2_52 | ISNN (1) |
Keywords | Field | DocType |
feature selection,statistical property,manifold learning,age estimation task,sensitivity analysis,prediction error estimation,k-fold cross-validation prediction error,experimental study,pal dataset,repeated cross-validation,prediction error,appropriate estimator,prediction error estimator | Mean squared prediction error,Feature selection,Computer science,Support vector machine,Mean squared error,Artificial intelligence,Nonlinear dimensionality reduction,Cross-validation,Machine learning,Estimator,Test set | Conference |
Citations | PageRank | References |
2 | 0.37 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cuixian Chen | 1 | 53 | 6.38 |
| Yishi Wang | 2 | 43 | 5.50 |
| Yaw Chang | 3 | 34 | 3.31 |
| Karl Ricanek | 4 | 165 | 18.65 |