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Forestry Advance Access originally published online on February 4, 2009
Forestry 2009 82(2):211-226; doi:10.1093/forestry/cpp001
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© Institute of Chartered Foresters, 2009. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

A Bayesian approach to classification accuracy inference

Steen Magnussen

Canadian Forest Service, West Burnside Road, Victoria V8Z 1M5 BC, Canada

Corresponding author. E-mail: steen.magnussen{at}nrcan.gc.ca


  Abstract

Bayesian accuracy assessments draw inference about random (super-population) parameters characterizing the classification process and accuracy statistics derived from these parameters. A conventional frequentist approach seeks to estimate the same parameters, but view them as fixed finite population quantities. Both approaches are detailed and contrasted with a real land cover data example. Bayesian results are given for non-informative and informative priors. The latter is justified in past experience. Results from simple and stratified random samplings on overall and class-specific accuracies and kappa coefficients of agreement are detailed for samples representing the 10 per cent, the 50 per cent and the 90 per cent quantile in a Monte Carlo sampling distribution of overall accuracy. A Bayesian approach is recommended for applications with small sample sizes and for quality assurance monitoring where prior data can boost effective sample sizes.

Received 25 June 2008.
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