Article
Alternating local search based VNS for linear classification
Author/s | Plastria, Frank
Bruyne, Steven de Carrizosa Priego, Emilio José |
Department | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Publication Date | 2010-02 |
Deposit Date | 2016-07-21 |
Published in |
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Abstract | We consider the linear classification method consisting of separating two sets of points in d-space by a hyperplane. We wish to determine the hyperplane which minimises the sum of distances from all misclassified points ... We consider the linear classification method consisting of separating two sets of points in d-space by a hyperplane. We wish to determine the hyperplane which minimises the sum of distances from all misclassified points to the hyperplane. To this end two local descent methods are developed, one grid-based and one optimisation-theory based, and are embedded in several ways into a VNS metaheuristic scheme. Computational results show these approaches to be complementary, leading to a single hybrid VNS strategy which combines both approaches to exploit the strong points of each. Extensive computational tests show that the resulting method performs well. |
Citation | Plastria, F., De Bruyne, S. y Carrizosa Priego, E.J. (2010). Alternating local search based VNS for linear classification. Annals of Operations Research, 174 (1), 121-134. |
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