ISNN 2012: Advances in Neural Networks – ISNN 2012 pp 458-467 | Cite as
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.
Keywords
Feature Selection Prediction Error Support Vector Regression Mean Absolute Error Locality Preserve ProjectionPreview
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