Resampling methods for parameter-free and robust feature selection with mutual information

TL;DR

This paper introduces resampling techniques like K-fold cross-validation and permutation tests to improve mutual information-based feature selection by making it parameter-free and more robust, especially in high-dimensional settings.

Contribution

It proposes a novel resampling-based approach to automatically tune parameters and determine stopping criteria in mutual information feature selection.

Findings

Resampling methods effectively estimate the variance of mutual information.

The approach automatically sets parameters and stopping thresholds.

Validated on synthetic and real-world datasets.

Abstract

Combining the mutual information criterion with a forward feature selection strategy offers a good trade-off between optimality of the selected feature subset and computation time. However, it requires to set the parameter(s) of the mutual information estimator and to determine when to halt the forward procedure. These two choices are difficult to make because, as the dimensionality of the subset increases, the estimation of the mutual information becomes less and less reliable. This paper proposes to use resampling methods, a K-fold cross-validation and the permutation test, to address both issues. The resampling methods bring information about the variance of the estimator, information which can then be used to automatically set the parameter and to calculate a threshold to stop the forward procedure. The procedure is illustrated on a synthetic dataset as well as on real-world examples.