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CORR
2007
Springer

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

13 years 9 months ago
Resampling methods for parameter-free and robust feature selection with mutual information
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 Kfold 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 data set as well as on the real-world examples. r 2007 Elsevier B.V. All rights reserved.
Damien François, Fabrice Rossi, Vincent Wer
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CORR
Authors Damien François, Fabrice Rossi, Vincent Wertz, Michel Verleysen
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