A statistical approach for separability of classesReportar como inadecuado

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1 ERIC - Equipe de Recherche en Ingénierie des Connaissances 2 LHC - Laboratoire Hubert Curien Saint Etienne

Abstract : We propose a new statistical approach for characterizing the class separability degree in R^{p}. This approach is based on a non-parametric statistic called the cut edge weight. We show in this paper the principle and the experimental applications of this statistic. First, we build a geometrical connected graph like Toussaint-s Relative Neighbourhood Graph on all examples of the learning set. Second, we cut all edges between two examples of a different class. Third, we compute the relative weight of these cut edges. If the relative weight of the cut edges is in the expected range of a random distribution of the labels on all the neighbourhood of the graph-s vertices, then no neighbourhood-based method provides a reliable prediction model. We will say then that the classes to predict are non-separable.

Keywords : separability supervised learning computational geometry

Autor: D.A. Zighed - Stéphane Lallich - Fabrice Muhlenbach -

Fuente: https://hal.archives-ouvertes.fr/


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