A high performance k-NN approach using binary neural networks.
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.
Hodge
Victoria J
VJ
Advanced Computer Architecture Group, Department of Computer Science, University of York, Heslington, York YO10 5DD, UK. vicky@cs.york.ac.uk
Lees
Ken J
KJ
Austin
James L
JL
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008568:Memory; D008606:Mental Processes / Q000502:physiology*; D016571:Neural Networks, Computer; D012984:Software; D013997:Time Factors