Classification using distance nearest neighbours

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Title: Classification using distance nearest neighbours
Authors: Friel, Nial
Pettitt, Anthony
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Date: 2010
Online since: 2010-09-03T14:04:50Z
Abstract: This paper proposes a new probabilistic classification algorithm using a Markov random field approach. The joint distribution of class labels is explicitly modelled using the distances between feature vectors. Intuitively, a class label should depend more on class labels which are closer in the feature space, than those which are further away. Our approach builds on previous work by Holmes and Adams (2002, 2003) and Cucala et al. (2009). Our work shares many of the advantages of these approaches in providing a probabilistic basis for the statistical inference. In comparison to previous work, we present a more efficient computational algorithm to overcome the intractability of the Markov random field model. The results of our algorithm are encouraging in comparison to the k-nearest neighbour algorithm.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: Springer
Journal: Statistics and Computing
Volume: 21
Issue: 3
Start page: 431
End page: 437
Copyright (published version): Springer, 2010
Keywords: ClassificationMarkov chain Monte Carlo
Subject LCSH: Classification
Monte Carlo method
Nearest neighbor analysis (Statistics)
DOI: 10.1007/s11222-010-9179-y
Other versions: The final publication is available at
Language: en
Status of Item: Peer reviewed
Appears in Collections:Mathematics and Statistics Research Collection

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