Random Voronoi ensembles for gene selection

Francesco Masulli (a,c), Stefano Rovetta (b,c)

(a) Dipartimento di Informatica, Universita' di Pisa, Italy
(b) Dipartimento di Informatica e Scienze dell’Informazione,
    Universita' di Genova, Italy
(c) Istituto Nazionale per la Fisica della Materia, Genova, Italy


Neurocomputing


ABSTRACT

The paper addresses the issue of assessing the importance of input variables
with respect to a given dichotomic classification problem. Both linear and
non-linear cases are considered. In the linear case, the application of
derivative-based saliency yields a commonly adopted ranking criterion. In the
non-linear case, the method is extended by introducing a resampling technique
and by clustering the obtained results for stability of the estimate. 

Keywords: Classifier combinations; Input selection; Quantization; DNA microarrays



2003 Published by Elsevier Science B.V.