Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters
Abstract
This study proposes and evaluates a new Bayesian network classifier (BNC) having an I-map structure with the fewest class variable parameters among all structures for which the class variable has no parent. Moreover, a new learning algorithm to learn our proposed model is presented. The proposed method is guaranteed to obtain the true classification probability asymptotically. Moreover, the method has lower computational costs than those of exact learning BNC using marginal likelihood. Comparison experiments have demonstrated the superior performance of the proposed method.
Cite
Text
Sugahara et al. "Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I18.30039Markdown
[Sugahara et al. "Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/sugahara2024aaai-learning/) doi:10.1609/AAAI.V38I18.30039BibTeX
@inproceedings{sugahara2024aaai-learning,
title = {{Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters}},
author = {Sugahara, Shouta and Kato, Koya and Ueno, Maomi},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {20540-20549},
doi = {10.1609/AAAI.V38I18.30039},
url = {https://mlanthology.org/aaai/2024/sugahara2024aaai-learning/}
}