Abstract

For an augmented Bayesian network classifier we propose a method of scoring a set of feature nodes for the separation strength, wherein we have combined a weighting technique and growing dimension asymptotics in a single framework. We show that the distribution of the weighted classifier is asymptotically Gaussian and establish the weight-function which is optimal in a sense of minimum misclassification probability

Keywords

Bayesian network; augmenting; separation strength; growing dimension asymptotic; weighted classifier; limiting error probability

Published in

Research report (Centre of Biostochastics)
2003, Volume: 2003, number: 6, pages: 1-18
Publisher: SLU

SLU Authors

Permanent link to this page (URI)

https://res.slu.se/id/publ/139