Maomi Ueno

JMLR Journal 2026 Journal Article

Learning Bayesian Network Classifiers to Minimize Class Variable Parameters

• Shouta Sugahara
• Koya Kato
• James Cussens
• Maomi Ueno

This study proposes and evaluates a novel Bayesian network classifier which can asymptotically estimate the true probability distribution of the class variable with the fewest class variable parameters among all structures for which the class variable has no parent. Moreover, to search for an optimal structure of the proposed classifier, we propose (1) a depth-first search based method and (2) an integer programming based method. The proposed methods are guaranteed to obtain the true probability distribution asymptotically while minimizing the number of class variable parameters. Comparative experiments using benchmark datasets demonstrate the effectiveness of the proposed method. [abs] [ pdf ][ bib ] &copy JMLR 2026. ( edit, beta )

AAAI Conference 2024 Conference Paper

Learning Bayesian Network Classifiers to Minimize the Class Variable Parameters

• Shouta Sugahara
• Koya Kato
• Maomi Ueno

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.

UAI Conference 2011 Conference Paper

Robust learning Bayesian networks for prior belief

• Maomi Ueno

UAI Conference 2010 Conference Paper

Learning networks determined by the ratio of prior and data

• Maomi Ueno

Recent reports have described that the equivalent sample size (ESS) in a Dirichlet prior plays an important role in learning Bayesian networks. This paper provides an asymptotic analysis of the marginal likelihood score for a Bayesian network. Results show that the ratio of the ESS and sample size determine the penalty of adding arcs in learning Bayesian networks. The number of arcs increases monotonically as the ESS increases; the number of arcs monotonically decreases as the ESS decreases. Furthermore, the marginal likelihood score provides a unified expression of various score metrics by changing prior knowledge.