Please use this identifier to cite or link to this item: https://ruomo.lib.uom.gr/handle/7000/343
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dc.contributor.authorOugiaroglou, Stefanos-
dc.contributor.authorEvangelidis, Georgios-
dc.date.accessioned2019-10-30T12:00:19Z-
dc.date.available2019-10-30T12:00:19Z-
dc.date.issued2016-02-
dc.identifier10.1007/s10044-014-0393-7en_US
dc.identifier.issn1433-7541en_US
dc.identifier.issn1433-755Xen_US
dc.identifier.urihttps://doi.org/10.1007/s10044-014-0393-7en_US
dc.identifier.urihttps://ruomo.lib.uom.gr/handle/7000/343-
dc.description.abstractAlthough the k -NN classifier is a popular classification method, it suffers from the high computational cost and storage requirements it involves. This paper proposes two effective cluster-based data reduction algorithms for efficient k -NN classification. Both have low preprocessing cost and can achieve high data reduction rates while maintaining k -NN classification accuracy at high levels. The first proposed algorithm is called reduction through homogeneous clusters (RHC) and is based on a fast preprocessing clustering procedure that creates homogeneous clusters. The centroids of these clusters constitute the reduced training set. The second proposed algorithm is a dynamic version of RHC that retains all its properties and, in addition, it can manage datasets that cannot fit in main memory and is appropriate for dynamic environments where new training data are gradually available. Experimental results, based on fourteen datasets, illustrate that both algorithms are faster and achieve higher reduction rates than four known methods, while maintaining high classification accuracy.en_US
dc.language.isoenen_US
dc.sourcePattern Analysis and Applicationsen_US
dc.subjectFRASCATI::Engineering and technologyen_US
dc.titleRHC: a non-parametric cluster-based data reduction for efficient k-NN classificationen_US
dc.typeArticleen_US
dc.contributor.departmentΤμήμα Εφαρμοσμένης Πληροφορικήςen_US
local.identifier.volume19en_US
local.identifier.issue1en_US
local.identifier.firstpage93en_US
local.identifier.lastpage109en_US
Appears in Collections:Department of Applied Informatics

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