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dc.contributor.authorGarcía López, Salvador 
dc.contributor.authorHerrera Triguero, Francisco
dc.identifier.citationGarcia, S., Cano, J. R., & Herrera, F. (2008). A memetic algorithm for evolutionary prototype selection: A scaling up approach. Pattern Recognition, 41(8), 2693-2709. doi:10.1016/j.patcog.2008.02.006es_ES
dc.description.abstractPrototype selection problem consists of reducing the size of databases by removing samples that are considered noisy or not influential on nearest neighbour classification tasks. Evolutionary algorithms have been used recently for prototype selection showing good results. However, due to the complexity of this problem when the size of the databases increases, the behaviour of evolutionary algorithms could deteriorate considerably because of a lack of convergence. This additional problem is known as the scaling up problem. Memetic algorithms are approaches for heuristic searches in optimization problems that combine a population-based algorithm with a local search. In this paper, we propose a model of memetic algorithm that incorporates an ad hoc local search specifically designed for optimizing the properties of prototype selection problem with the aim of tackling the scaling up problem. In order to check its performance, we have carried out an empirical study including a comparison between our proposal and previous evolutionary and non-evolutionary approaches studied in the literature. The results have been contrasted with the use of non-parametric statistical procedures and show that our approach outperforms previously studied methods, especially when the database scales up.es_ES
dc.rightsAtribución 3.0 España*
dc.subjectData reductiones_ES
dc.subjectEvolutionary algorithmses_ES
dc.subjectMemetic algorithmses_ES
dc.subjectPrototype selectiones_ES
dc.subjectScaling upes_ES
dc.subjectNearest neighbour rulees_ES
dc.subjectData mininges_ES
dc.titleA memetic algorithm for evolutionary prototype selection: A scaling up approaches_ES

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Atribución 3.0 España
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