Statistical dependence measure for feature selection in microarray datasets

Verónica Bolón-Canedo, Sohan Seth, Noelia Sánchez-Marono, Amparo Alonso-Betanzos, Jose C Principe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationESANN 2011 19th European Symposium on Artificial Neural Networks
Number of pages6
Publication statusPublished - 2011


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