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Authors: Takuto Enomoto and Masaomi Kimura

Affiliation: Shibaura Institute of Technology, Japan

ISBN: 978-989-758-158-8

ISSN: 2184-3228

Keyword(s): Bayesian Network, Association Rule Mining, Propositional Logic.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Foundations of Knowledge Discovery in Databases ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Soft Computing ; Symbolic Systems

Abstract: 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.

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Paper citation in several formats:
Enomoto, T. and Kimura, M. (2015). Construction of a Bayesian Network as an Extension of Propositional Logic. In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8 ISSN 2184-3228, pages 211-217. DOI: 10.5220/0005595102110217

@conference{kdir15,
author={Takuto Enomoto. and Masaomi Kimura.},
title={Construction of a Bayesian Network as an Extension of Propositional Logic},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={211-217},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005595102110217},
isbn={978-989-758-158-8},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Construction of a Bayesian Network as an Extension of Propositional Logic
SN - 978-989-758-158-8
IS - 2184-3228
AU - Enomoto, T.
AU - Kimura, M.
PY - 2015
SP - 211
EP - 217
DO - 10.5220/0005595102110217

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