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

Adil Bagirov (), Julien Ugon, Dean Webb, Gurkan Ozturk and Refail Kasimbeyli

TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, 2013, vol. 21, issue 1, 3-24

Abstract: 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. Copyright Sociedad de Estadística e Investigación Operativa 2013

Keywords: Nonsmooth optimization; Piecewise linear separability; Data mining; Supervised learning; Piecewise linear classifiers; 65K05; 90C25 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11750-011-0241-5

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