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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.
ISBN: 978-3-642-28930-9
ISSN: 0302-9743
Other Identifiers: 10.1007/978-3-642-28931-6_16
Appears in Collections:Department of Applied Informatics

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