2007/01/01 by Damien François, D. François, Fabrice Rossi +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Evolutionary Algorithms and Applications #Face and Expression Recognition #Gene expression and cancer classification #cs.LG #stat.AP
paper · pdf · doi:10.1016/j.neucom.2006.11.019
published as Neurocomputing 70, 7-9 (2007) 1276-1288
openalex publication_date 2007/01/01 · arxiv created 2007/09/23 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.