Discretization methods for bayesian networks in the case of the earthquake

Sari, Devni Prima and Rosadi, Dedi and Effendie, Adhitya Ronnie and Danardono, Danardono (2021) Discretization methods for bayesian networks in the case of the earthquake. Bulletin of Electrical Engineering and Informatics, 10 (1). 299 – 307. ISSN 20893191

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Abstract

The Bayesian networks are a graphical probability model that represents interactions between variables. This model has been widely applied in various fields, including in the case of disaster. In applying field data, we often find a mixture of variable types, which is a combination of continuous variables and discrete variables. For data processing using hybrid and continuous Bayesian networks, all continuous variables must be normally distributed. If normal conditions unsatisfied, we offer a solution, is to discretize continuous variables. Next, we can continue the process with the discrete Bayesian networks. The discretization of a variable can be done in various ways, including equal-width, equal-frequency, and K-means. The combination of BN and k-means is a new contribution in this study called the k-means Bayesian networks (KMBN) model. In this study, we compared the three methods of discretization used a confusion matrix. Based on the earthquake damage data, the K-means clustering method produced the highest level of accuracy. This result indicates that K-means is the best method for discretizing the data that we use in this study. © 2021, Institute of Advanced Engineering and Science. All rights reserved.

Item Type: Article
Additional Information: Cited by: 3; All Open Access, Gold Open Access
Uncontrolled Keywords: Bayesian networks; Earthquake; Equal-frequency; Equal-width; K-means
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Mathematics and Natural Sciences > Mathematics Department
Depositing User: Sri JUNANDI
Date Deposited: 25 Oct 2024 01:11
Last Modified: 25 Oct 2024 01:11
URI: https://ir.lib.ugm.ac.id/id/eprint/8600

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