Differential Evolution Classifier with Optimized OWA-Based Multi-distance Measures for the Features in the Data Sets

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dc.contributor.authorKoloseni, David
dc.contributor.authorFedrizzi, Mario
dc.contributor.authorLuukka, Pasi
dc.contributor.authorLampinen, Jouni
dc.contributor.authorCollan, Mikael
dc.date.accessioned2016-09-21T14:26:02Z
dc.date.available2016-09-21T14:26:02Z
dc.date.issued2015
dc.descriptionFull text can be accessed at http://link.springer.com/chapter/10.1007/978-3-319-11313-5_67en_US
dc.description.abstractThis paper introduces a new classification method that uses the differential evolution algorithm to feature-wise select, from a pool of distance measures, an optimal distance measure to be used for classification of elements. The distances yielded for each feature by the optimized distance measures are aggregated into an overall distance vector for each element by using OWA based multi-distance aggregation.en_US
dc.identifier.citationKoloseni, D., Fedrizzi, M., Luukka, P., Lampinen, J. and Collan, M., 2015. Differential Evolution Classifier with Optimized OWA-Based Multi-distance Measures for the Features in the Data Sets. In Intelligent Systems' 2014 (pp. 765-777). Springer International Publishing.en_US
dc.identifier.doi10.1007/978-3-319-11313-5_67
dc.identifier.urihttp://hdl.handle.net/20.500.11810/4039
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.subjectClassificationen_US
dc.subjectDifferential evolutionen_US
dc.subjectPool of distancesen_US
dc.subjectMulti-distancesen_US
dc.titleDifferential Evolution Classifier with Optimized OWA-Based Multi-distance Measures for the Features in the Data Setsen_US
dc.typeBook chapteren_US
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