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Probability Estimation for Bayesian Network Classifiers and Decision Trees

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thesis
posted on 13.07.2020, 00:42 by HE ZHANG
This thesis aims to improve the class probability estimation of Bayesian network classifiers and decision trees by hierarchical probability smoothing and ensemble learning techniques. Two new algorithms are proposed in this thesis with the state-of-the-art performance, which is an important contribution for applications that accurate class probability estimates are essential.

History

Campus location

Australia

Principal supervisor

Wray Buntine

Additional supervisor 1

Francois Petitjean

Year of Award

2020

Department, School or Centre

Clayton School of IT

Course

Doctor of Philosophy

Degree Type

DOCTORATE