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Alternating local search based VNS for linear classification

 

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Author: Plastria, Frank
Bruyne, Steven de
Carrizosa Priego, Emilio José
Department: Universidad de Sevilla. Departamento de Estadística e Investigación Operativa
Date: 2010-02
Published in: Annals of Operations Research, 174 (1), 121-134.
Document type: Article
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 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.
Cite: 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.
Size: 216.2Kb
Format: PDF

URI: http://hdl.handle.net/11441/43897

DOI: 10.1007/s10479-009-0538-z

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