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A binary neural k-nearest neighbour technique

A binary neural k-nearest neighbour technique K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record in turn. This paper evaluates a novel k-NN classifier with linear growth and faster run-time built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and real-valued data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Knowledge and Information Systems Springer Journals

A binary neural k-nearest neighbour technique

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References (20)

Publisher
Springer Journals
Copyright
Copyright © 2005 by Springer-Verlag
Subject
Computer Science; Business Information Systems; Information Systems and Communication Service
ISSN
0219-1377
eISSN
0219-3116
DOI
10.1007/s10115-004-0191-4
Publisher site
See Article on Publisher Site

Abstract

K-Nearest Neighbour (k-NN) is a widely used technique for classifying and clustering data. K-NN is effective but is often criticised for its polynomial run-time growth as k-NN calculates the distance to every other record in the data set for each record in turn. This paper evaluates a novel k-NN classifier with linear growth and faster run-time built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and real-valued data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall the k-best matches. We compare various configurations of the binary approach to a conventional approach for memory overheads, training speed, retrieval speed and retrieval accuracy. We demonstrate the superior performance with respect to speed and memory requirements of the binary approach compared to the standard approach and we pinpoint the optimal configurations.

Journal

Knowledge and Information Systems – Springer Journals

Published: Feb 2, 2005

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