A statistical approach to class separability

Djamel A. Zighed, Stéphane Lallich and Fabrice Muhlenbach

Applied Stochastic Models in Business and Industry, 2005, vol. 21, issue 2, 187-197

Abstract: We propose a new statistical approach for characterizing the class separability degree in ℝp. 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. Copyright © 2005 John Wiley & Sons, Ltd.

Date: 2005
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https://doi.org/10.1002/asmb.532

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Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:21:y:2005:i:2:p:187-197

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