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Nearest convex hull classification

Author

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  • Nalbantov, G.I.
  • Groenen, P.J.F.
  • Bioch, J.C.

Abstract

Consider the classification task of assigning a test object to one of two or more possible groups, or classes. An intuitive way to proceed is to assign the object to that class, to which the distance is minimal. As a distance measure to a class, we propose here to use the distance to the convex hull of that class. Hence the name Nearest Convex Hull (NCH) classification for the method. Convex-hull overlap is handled through the introduction of slack variables and kernels. In spirit and computationally the method is therefore close to the popular Support Vector Machine (SVM) classifier. Advantages of the NCH classifier are its robustness to outliers, good regularization properties and relatively easy handling of multi-class problems. We compare the performance of NCH against state-of-art techniques and report promising results.

Suggested Citation

  • Nalbantov, G.I. & Groenen, P.J.F. & Bioch, J.C., 2006. "Nearest convex hull classification," Econometric Institute Research Papers EI 2006-50, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    • Handle: RePEc:ems:eureir:8217
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    File URL: https://repub.eur.nl/pub/8217/NCH10.pdf
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    Cited by:

    1. James Chok & Geoffrey M. Vasil, 2023. "Convex optimization over a probability simplex," Papers 2305.09046, arXiv.org.

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