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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.