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.
Bibliographical noteCopyright © 2005 Springer-Verlag. This is an author produced version of a paper published in Knowledge and Information Systems. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.The original publication is available at www.springerlink.com.
- binary neural network
- correlation matrix memory
- K-nearest neighbour
- parabolic kernel