Case based imprecision estimates for Bayes classifiers with the Bayesian bootstrap

This article outlines a Bayesian bootstrap method for case based imprecision estimates in Bayes classification. We argue that this approach is an important complement to methods such as k-fold cross validation that are based on overall error rates. It is shown how case based imprecision estimates ma...

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Bibliographic Details
Published in:Machine learning 2005, Vol.58 (1), p.79-94
Main Authors: NOREN, G. Niklas, ORRE, Roland
Format: Article
Language:English
Subjects:
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Exact sciences and technology
Learning and adaptive systems
Mathematical models
Mathematics
Multivariate analysis
Probability and statistics
Sciences and techniques of general use
Statistics
Studies
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Description
Summary:This article outlines a Bayesian bootstrap method for case based imprecision estimates in Bayes classification. We argue that this approach is an important complement to methods such as k-fold cross validation that are based on overall error rates. It is shown how case based imprecision estimates may be used to improve Bayes classifiers under asymmetrical loss functions. In addition, other approaches to making use of case based imprecision estimates are discussed and illustrated on two real world data sets. Contrary to the common assumption, Bayesian bootstrap simulations indicate that the uncertainty associated with the output of a Bayes classifier is often far from normally distributed.[PUBLICATION ABSTRACT]
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-005-5010-y