A Bayesian reassessment of nearest-neighbour classification

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dc.creatorCucala, Lionel
dc.creatorMarin, Jean-Michel
dc.creatorRobert, Christian
dc.creatorTitterington, Mike
dc.date2008-02-10
dc.date.accessioned2026-07-07T09:19:50Z
dc.date.available2026-07-07T09:19:50Z
dc.descriptionThe k-nearest-neighbour procedure is a well-known deterministic method used in supervised classification. This paper proposes a reassessment of this approach as a statistical technique derived from a proper probabilistic model; in particular, we modify the assessment made in a previous analysis of this method undertaken by Holmes and Adams (2002,2003), and evaluated by Manocha and Girolami (2007), where the underlying probabilistic model is not completely well-defined. Once a clear probabilistic basis for the k-nearest-neighbour procedure is established, we derive computational tools for conducting Bayesian inference on the parameters of the corresponding model. In particular, we assess the difficulties inherent to pseudo-likelihood and to path sampling approximations of an intractable normalising constant, and propose a perfect sampling strategy to implement a correct MCMC sampler associated with our model. If perfect sampling is not available, we suggest using a Gibbs sampling approximation. Illustrations of the performance of the corresponding Bayesian classifier are provided for several benchmark datasets, demonstrating in particular the limitations of the pseudo-likelihood approximation in this set-up.
dc.identifierhttps://arxiv.org/abs/0802.1357
dc.identifierhttp://arxiv.org/abs/0802.1357
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/154538
dc.subjectComputation
dc.subjectStatistics Theory
dc.titleA Bayesian reassessment of nearest-neighbour classification
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