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
Full text not available from this repository. (Request a copy)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 |
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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 |