Differential Evolution Based Nearest Prototype Classifier with Optimized Distance Measures for the Features in the Data Sets

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dc.contributor.authorKoloseni, David
dc.contributor.authorLampinen, Jouni
dc.contributor.authorLuukka, Pasi
dc.date.accessioned2016-09-21T17:13:15Z
dc.date.available2016-09-21T17:13:15Z
dc.date.issued2013
dc.description.abstractIn this paper a further generalization of differential evolution based data classification method is proposed, demonstrated and initially evaluated. The differential evolution classifier is a nearest prototype vector based classifier that applies a global optimization algorithm, differential evolution, for determining the optimal values for all free parameters of the classifier model during the training phase of the classifier. The earlier version of differential evolution classifier that applied individually optimized distance measure for each new data set to be classified is generalized here so, that instead of optimizing a single distance measure for the given data set, we take a further step by proposing an approach where distance measures are optimized individually for each feature of the data set to be classified. In particular, distance measures for each feature are selected optimally from a predefined pool of alternative distance measures. The optimal distance measures are determined by differential evolution algorithm, which is also determining the optimal values for all free parameters of the selected distance measures in parallel. After determining the optimal distance measures for each feature together with their optimal parameters, we combine all featurewisely determined distance measures to form a single total distance measure, that is to be applied for the final classification decisions. The actual classification process is still based on the nearest prototype vector principle; A sample belongs to the class represented by the nearest prototype vector when measured with the above referred optimized total distance measure. During the training process the differential evolution algorithm determines optimally the class vectors, selects optimal distance metrics for each data feature, and determines the optimal values for the free parameters of each selected distance measure. Based on experimental results with nine well known classification benchmark data sets, the proposed approach yield a statistically significant improvement to the classification accuracy of differential evolution classifier.en_US
dc.identifier.citationKoloseni, D., Lampinen, J. and Luukka, P., 2013. Differential evolution based nearest prototype classifier with optimized distance measures for the features in the data sets. Expert Systems with Applications, 40(10), pp.4075-4082.en_US
dc.identifier.doi10.1016/j.eswa.2013.01.040
dc.identifier.urihttp://hdl.handle.net/20.500.11810/4144
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectDifferential evolutionen_US
dc.subjectClassificationen_US
dc.subjectDistance measuresen_US
dc.subjectDistance selection for the featureen_US
dc.subjectPool of distancesen_US
dc.titleDifferential Evolution Based Nearest Prototype Classifier with Optimized Distance Measures for the Features in the Data Setsen_US
dc.typeJournal Article, Peer Revieweden_US
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