A novel piecewise linear classifier based on polyhedral conic and max–min separabilities

• Adil M. Bagirov ^[1] ; Julien Ugon ^[1] ; Dean Webb ^[1] ; Gurkan Ozturk ^[2] ; Refail Kasimbeyli ^[3]
  1. [1] Federation University

     Federation University

     Australia

     
  2. [2] Anadolu University

     Anadolu University

     Turquía

     
  3. [3] İzmir University of Economics

     İzmir University of Economics

     Turquía

     

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• Localización: Top, ISSN-e 1863-8279, ISSN 1134-5764, Vol. 21, Nº. 1, 2013, págs. 3-24
• Idioma: inglés
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• Resumen

  • In this paper, an algorithm for finding piecewise linear boundaries between pattern classes is developed. This algorithm consists of two main stages. In the first stage, a polyhedral conic set is used to identify data points which lie inside their classes, and in the second stage we exclude those points to compute a piecewise linear boundary using the remaining data points. Piecewise linear boundaries are computed incrementally starting with one hyperplane. Such an approach allows one to significantly reduce the computational effort in many large data sets. Results of numerical experiments are reported. These results demonstrate that the new algorithm consistently produces a good test set accuracy on most data sets comparing with a number of other mainstream classifiers.