### 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.

Language | English |
---|---|

Title of host publication | KDIR |

Publisher | SciTePress |

Pages | 211-217 |

Number of pages | 7 |

Volume | 1 |

ISBN (Print) | 9789897581588 |

State | Published - 2015 |

Event | 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 - Lisbon, Portugal Duration: 2015 Nov 12 → 2015 Nov 14 |

### Other

Other | 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015 |
---|---|

Country | Portugal |

City | Lisbon |

Period | 15/11/12 → 15/11/14 |

### Fingerprint

### Keywords

- Association rule mining
- Bayesian network
- Propositional logic

### ASJC Scopus subject areas

- Software

### Cite this

*KDIR*(Vol. 1, pp. 211-217). SciTePress.

**Construction of a Bayesian network as an extension of propositional logic.** / Enomoto, Takuto; Kimura, Masaomi.

Research output: Research › Conference contribution

*KDIR.*vol. 1, SciTePress, pp. 211-217, 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, IC3K 2015, Lisbon, Portugal, 15/11/12.

}

TY - CHAP

T1 - Construction of a Bayesian network as an extension of propositional logic

AU - Enomoto,Takuto

AU - Kimura,Masaomi

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Association rule mining

KW - Bayesian network

KW - Propositional logic

UR - http://www.scopus.com/inward/record.url?scp=84960883615&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84960883615&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9789897581588

VL - 1

SP - 211

EP - 217

BT - KDIR

PB - SciTePress

ER -