A high performance k-NN approach using binary neural networks

Authors:
Victoria J. Hodge;Ken J. Lees;James L. Austin
Affiliations:
Advanced Computer Architecture Group, Department of Computer Science, University of York, Heslington, York YO10 5DD, UK;Advanced Computer Architecture Group, Department of Computer Science, University of York, Heslington, York YO10 5DD, UK;Advanced Computer Architecture Group, Department of Computer Science, University of York, Heslington, York YO10 5DD, UK
Venue:
Neural Networks
Year:
2004

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Abstract

This paper evaluates a novel k-nearest neighbour (k-NN) classifier built from binary neural networks. The binary neural approach uses robust encoding to map standard ordinal, categorical and numeric data sets onto a binary neural network. The binary neural network uses high speed pattern matching to recall a candidate set of matching records, which are then processed by a conventional k-NN approach to determine 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.