Construction of a Bayesian network as an extension of propositional logic

Takuto Enomoto, Masaomi Kimura

研究成果: Conference contribution

抄録

A Bayesian network is a probabilistic graphical model. Many conventional methods have been proposed for its construction. However, these methods often result in an incorrect Bayesian network structure. In this study, to correctly construct a Bayesian network, we extend the concept of propositional logic. We propose a methodology for constructing a Bayesian network with causal relationships that are extracted only if the antecedent states are true. In order to determine the logic to be used in constructing the Bayesian network, we propose the use of association rule mining such as the Apriori algorithm. We evaluate the proposed method by comparing its result with that of traditional method, such as Bayesian Dirichlet equivalent uniform (BDeu) score evaluation with a hill climbing algorithm, that shows that our method generates a network with more necessary arcs than that generated by the traditional method.

本文言語English
ホスト出版物のタイトルKDIR
編集者Ana Fred, Jan Dietz, David Aveiro, Kecheng Liu, Joaquim Filipe, Joaquim Filipe
出版社SciTePress
ページ211-217
ページ数7
ISBN(電子版)9789897581588
DOI
出版ステータスPublished - 2015
イベント7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal
継続期間: 2015 11 122015 11 14

出版物シリーズ

名前IC3K 2015 - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management
1

Other

Other7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015
国/地域Portugal
CityLisbon
Period15/11/1215/11/14

ASJC Scopus subject areas

  • ソフトウェア

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