Discretization methods for Bayesian networks in the case of the earthquake
| Autor: | Dedi Rosadi, Danardono Danardono, Adhitya Ronnie Effendie, Devni Prima Sari |
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| Rok vydání: | 2021 |
| Předmět: |
Data processing
Control and Optimization Earthquake Discretization Equal-width Computer Networks and Communications Computer science k-means clustering Process (computing) Bayesian network Confusion matrix Variable (computer science) Bayesian networks Hardware and Architecture Control and Systems Engineering Computer Science (miscellaneous) Equal-frequency Electrical and Electronic Engineering Cluster analysis Instrumentation Algorithm K-means Information Systems |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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