ACADSTAFF UGM
| Title | : | Discretization methods for Bayesian networks in the case of the earthquake |
| Author | : |
DEVNI PRIMA SARI (1) Prof. Dr.rer.nat. Dedi Rosadi, S.Si., M.Sc. (2) Dr. Adhitya Ronnie Effendie, S.Si., M.Si., M.Sc. (3) Drs. Danardono, MPH., Ph.D. (4) |
| Date | : | 1 2021 |
| Keyword | : | Bayesian networks,Earthquake,Equal-frequency,Equal-width,K-means Bayesian networks,Earthquake,Equal-frequency,Equal-width,K-means |
| 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. |
| Group of Knowledge | : | Statistik |
| Original Language | : | English |
| Level | : | Internasional |
| Status | : |
Published
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| No | Title | Action |
|---|---|---|
| 1 |
BEEI Devni.pdf
Document Type : [PAK] Full Dokumen
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