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dc.contributor.authorKrawczyk, Bartosz
dc.contributor.authorTriguero, Isaac
dc.contributor.authorGarcía López, Salvador 
dc.contributor.authorWozniak, Michal
dc.contributor.authorHerrera Triguero, Francisco 
dc.date.accessioned2020-12-11T13:32:28Z
dc.date.available2020-12-11T13:32:28Z
dc.date.issued2019-06
dc.identifier.citationPublisher version: Krawczyk, B., Triguero, I., García, S. et al. Instance reduction for one-class classification. Knowl Inf Syst 59, 601–628 (2019). [https://doi.org/10.1007/s10115-018-1220-z]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/64848
dc.description.abstractInstance reduction techniques are data preprocessing methods originally developed to enhance the nearest neighbor rule for standard classification. They reduce the training data by selecting or generating representative examples of a given problem. These algorithms have been designed and widely analyzed in multi-class problems providing very competitive results. However, this issue was rarely addressed in the context of one-class classification. In this specific domain a reduction of the training set may not only decrease the classification time and classifier's complexity, but also allows us to handle internal noisy data and simplify the data description boundary. We propose two methods for achieving this goal. The first one is a flexible framework that adjusts any instance reduction method to one-class scenario by introduction of meaningful artificial outliers. The second one is a novel modification of evolutionary instance reduction technique that is based on differential evolution and uses consistency measure for model evaluation in filter or wrapper modes. It is a powerful native one-class solution that does not require an access to counterexamples. Both of the proposed algorithms can be applied to any type of one-class classifier. On the basis of extensive computational experiments, we show that the proposed methods are highly efficient techniques to reduce the complexity and improve the classification performance in one-class scenarios.es_ES
dc.description.sponsorshipPolish National Science Center UMO-2015/19/B/ST6/01597es_ES
dc.description.sponsorshipSpanish National Research Project TIN2014-57251-Pes_ES
dc.description.sponsorshipAndalusian Research Plan P11-TIC-7765es_ES
dc.language.isoenges_ES
dc.publisherSpringer Naturees_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectMachine learninges_ES
dc.subjectOne-class classificationes_ES
dc.subjectInstance reductiones_ES
dc.subjectTraining set selectiones_ES
dc.subjectEvolutionary computinges_ES
dc.titleInstance Reduction for One-Class Classificationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.1007/s10115-018-1220-z
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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