A high performance k-NN approach using binary neural networks

Research output: Contribution to journalArticlepeer-review


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. (C) 2003 Elsevier Ltd. All rights reserved.

Original languageEnglish
Pages (from-to)441-458
Number of pages17
JournalNeural Networks
Issue number3
Publication statusPublished - Apr 2004

Bibliographical note

Copyright © 2003 Elsevier Ltd. This is an author produced version of a paper published in Neural Networks. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.


  • binary neural network
  • associative memory
  • correlation matrix memory
  • k-nearest neighbour
  • euclidean distance
  • robust encoding
  • quantisation
  • binary mapping

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