Abstract
In this paper we propose a flexible method for analyzing the relevance of input variables in high dimensional problems 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. The method was preliminarly validated on the data published by T.R. Golub et al. on a study, at the molecular level, of two kinds of leukemia: Acute Myeloid Leukemia and Acute Lymphoblastic Leukemia (Science 5439-286, 531-537, 1999). Our technique indicates that, among the top 20 genes found by the final cluster analysis, 8 of the 50 genes listed in the original work feature a stronger discriminating power.
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Masulli, F., Rovetta, S. (2003). Gene Selection Using Random Voronoi Ensembles. In: Apolloni, B., Marinaro, M., Tagliaferri, R. (eds) Neural Nets. WIRN 2003. Lecture Notes in Computer Science, vol 2859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45216-4_34
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DOI: https://doi.org/10.1007/978-3-540-45216-4_34
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