Please use this identifier to cite or link to this item:
https://ruomo.lib.uom.gr/handle/7000/1205
Title: | An Adaptive Hybrid and Cluster-Based Model for Speeding Up the k-NN Classifier |
Authors: | Ougiaroglou, Stefanos Evangelidis, Georgios Dervos, Dimitris A. |
Editors: | Corchado, E. Snášel, V. Abraham, A. Woźniak, M. Graña, M. Cho, SB. |
Type: | Conference Paper |
Subjects: | FRASCATI::Natural sciences::Computer and information sciences |
Keywords: | k-NN classifier cluster-based classification data reduction |
Issue Date: | 2012 |
Volume: | 7209 |
First Page: | 163 |
Last Page: | 175 |
Volume Title: | Hybrid Artificial Intelligent Systems |
Part of Series: | Lecture Notes in Computer Science |
Part of Series: | Lecture Notes in Computer Science |
Abstract: | A well known classification method is the k-Nearest Neighbors (k-NN) classifier. However, sequentially searching for the nearest neighbors in large datasets downgrades its performance because of the high computational cost involved. This paper proposes a cluster-based classification model for speeding up the k-NN classifier. The model aims to reduce the cost as much as possible and to maintain the classification accuracy at a high level. It consists of a simple data structure and a hybrid, adaptive algorithm that accesses this structure. Initially, a preprocessing clustering procedure builds the data structure. Then, the proposed algorithm, based on user-defined acceptance criteria, attempts to classify an incoming item using the nearest cluster centroids. Upon failure, the incoming item is classified by searching for the k nearest neighbors within specific clusters. The proposed approach was tested on five real life datasets. The results show that it can be used either to achieve a high accuracy with gains in cost or to reduce the cost at a minimum level with slightly lower accuracy. |
URI: | https://doi.org/10.1007/978-3-642-28931-6_16 https://ruomo.lib.uom.gr/handle/7000/1205 |
ISBN: | 978-3-642-28930-9 978-3-642-28931-6 |
ISSN: | 0302-9743 1611-3349 |
Other Identifiers: | 10.1007/978-3-642-28931-6_16 |
Appears in Collections: | Department of Applied Informatics |
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2012_HAIS1.pdf | 291,68 kB | Adobe PDF | View/Open |
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