Alternating local search based VNS for linear classification
Bruyne, Steven de
Carrizosa Priego, Emilio José
|Department||Universidad de Sevilla. Departamento de Estadística e Investigación Operativa|
|Published in||Annals of Operations Research, 174 (1), 121-134.|
|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.
|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.|
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