Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management
Abstract
This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classification, the performance of these network models may be very poor. We demonstrate that Bayesian network models should be created to meet the specific goal or purpose intended for the model. We first present a goal-oriented algorithm for constructing Bayesian networks for predicting uncollectibles in telecommunications riskmanagement datasets. Second, we argue and demonstrate that current Bayesian network learning methods may fail to perform satisfactorily in real life applications since they do not learn models tailored to a specific goal or purpose. Third, we discuss the performance of "goal oriented"...
Cite
Text
Ezawa et al. "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management." International Conference on Machine Learning, 1996.Markdown
[Ezawa et al. "Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management." International Conference on Machine Learning, 1996.](https://mlanthology.org/icml/1996/ezawa1996icml-learning/)BibTeX
@inproceedings{ezawa1996icml-learning,
title = {{Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management}},
author = {Ezawa, Kazuo J. and Singh, Moninder and Norton, Steven W.},
booktitle = {International Conference on Machine Learning},
year = {1996},
pages = {139-147},
url = {https://mlanthology.org/icml/1996/ezawa1996icml-learning/}
}