Bayesian networks based rare event prediction with sensor data

Authors:
Seong-Pyo Cheon;Sungshin Kim;So-Young Lee;Chong-Bum Lee
Affiliations:
School of Electrical and Computer Engineering, Pusan National University, 609735 Busan, Republic of Korea;School of Electrical and Computer Engineering, Pusan National University, 609735 Busan, Republic of Korea;Department of Atmospheric Sciences, Pusan National University, 609735 Busan, Republic of Korea;Department of Environment Engineering, Kangwon National University, Kangwon-do, Republic of Korea
Venue:
Knowledge-Based Systems
Year:
2009

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Abstract

A Bayesian network is a powerful graphical model. It is advantageous for real-world data analysis and finding relations among variables. Knowledge presentation and rule generation, based on a Bayesian approach, have been studied and reported in many research papers across various fields. Since a Bayesian network has both causal and probabilistic semantics, it is regarded as an ideal representation to combine background knowledge and real data. Rare event predictions have been performed using several methods, but remain a challenge. We design and implement a Bayesian network model to forecast daily ozone states. We evaluate the proposed Bayesian network model, comparing it to traditional decision tree models, to examine its utility.