Alternating local search based VNS

Frank Plastria(Frank.Plastriavub.ac.be)
Steven De Bruyne(Steven.De.Bruynevub.ac.be)
Emilio Carrizosa(ecarrizosaus.es)

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.

Keywords: Data Mining, Classification, Linear Classification, Heuristic Minimisation, Normdistance, Variable Neighbourhood Search, Variable Neighborhood Search, VNS, Local Search, Grid Search, Cell Search

Category 1: Applications -- Science and Engineering (Data-Mining )

Category 2: Combinatorial Optimization (Meta Heuristics )

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Entry Submitted: 02/19/2008
Entry Accepted: 02/19/2008
Entry Last Modified: 02/19/2008

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