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A dissimilarity-based approach for Classification


  • Carrizosa,Emilio
  • Martín-Barragán,Belén
  • Plastria,Frank
  • Romero Morales,Dolores



The Nearest Neighbor classifier has shown to be a powerful tool for multiclass classification. In this note we explore both theoretical properties and empirical behavior of a variant of such method, in which the Nearest Neighbor rule is applied after selecting a set of so-called prototypes, whose cardinality is fixed in advance, by minimizing the empirical mis-classification cost. With this we alleviate the two serious drawbacks of the Nearest Neighbor method: high storage requirements and time-consuming queries. The problem is shown to be NP-Hard. Mixed Integer Programming (MIP) programs are formulated, theoretically compared and solved by a standard MIP solver for problem instances of small size. Large sized problem instances are solved by a metaheuristic yielding good classification rules in reasonable time.

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  • Carrizosa,Emilio & Martín-Barragán,Belén & Plastria,Frank & Romero Morales,Dolores, 2005. "A dissimilarity-based approach for Classification," Research Memorandum 045, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
  • Handle: RePEc:unm:umamet:2005045

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    References listed on IDEAS

    1. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    2. Freed, Ned & Glover, Fred, 1981. "Simple but powerful goal programming models for discriminant problems," European Journal of Operational Research, Elsevier, vol. 7(1), pages 44-60, May.
    3. Plastria, Frank, 2002. "Formulating logical implications in combinatorial optimisation," European Journal of Operational Research, Elsevier, vol. 140(2), pages 338-353, July.
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    operations research and management science;

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