Feature selection is the domain of machine learning which studies data-driven methods to select, among a set of input variables, the ones that will lead to the most accurate predictive model. In this paper, a statistical dependence measure is presented for variable selection in the context of classification. Its performance is tested over DNA microarray data, a challenging dataset for machine learning researchers due to the high number of genes and relatively small number of measurements. This measure is compared against the so called mRMR approach, and is shown to obtain better or equal performance over the binary datasets.
|Title of host publication||ESANN 2011 19th European Symposium on Artificial Neural Networks|
|Number of pages||6|
|Publication status||Published - 2011|