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A statistical approach to class separability

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  • Djamel A. Zighed
  • Stéphane Lallich
  • Fabrice Muhlenbach

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.

Suggested Citation

  • Djamel A. Zighed & Stéphane Lallich & Fabrice Muhlenbach, 2005. "A statistical approach to class separability," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(2), pages 187-197, March.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:2:p:187-197
    DOI: 10.1002/asmb.532
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