An ensemble approach to variable selection for classification
of DNA microarray data

Francesco Masulli and Stefano Rovetta



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 nonlinear case, the method is extended
by introducing a resampling technique and by clustering
the obtained results for stability of the estimate. The work
is preliminary, and many properties and options are
to be investigated in future research.


Proceedings of the 2003 International Joint Conference on Neural networks