2018
DOI: 10.32890/jict2018.17.2.1
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Fuzzy Discretization Technique for Bayesian Flood Disaster Model

Abstract: The use of Bayesian Networks in the domain of disaster management has proven its efficiency in developing the disaster model and has been widely used to represent the logical relationships between variables. Prior to modelling the correlation between the flood factors, it was necessary to discretize the continuous data due to the weakness of the Bayesian Network to handle such variables. Therefore, this paper aimed to propose a data discretization technique and compare the existing discretization techniques to… Show more

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Cited by 2 publications
(2 citation statements)
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“…Data mining is a well-known technique for summarizing data by finding patterns that can be understood and useful for data owners. Specifically, transforming continuous data using discretization in the preprocessing techniques is found in many research works for spatial data mining (Ahmad-Azani et al, 2018). Hence many researchers use data mining in analyzing traffic accident datasets such as association rule, K-Means clustering and machine learning algorithm of the decision tree, naive Bayes, and K-nearest neighbour classifiers to predict accident severity (Beshah & Hill, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…Data mining is a well-known technique for summarizing data by finding patterns that can be understood and useful for data owners. Specifically, transforming continuous data using discretization in the preprocessing techniques is found in many research works for spatial data mining (Ahmad-Azani et al, 2018). Hence many researchers use data mining in analyzing traffic accident datasets such as association rule, K-Means clustering and machine learning algorithm of the decision tree, naive Bayes, and K-nearest neighbour classifiers to predict accident severity (Beshah & Hill, 2010).…”
Section: Introductionmentioning
confidence: 99%
“…As real-world data and problems can be imprecise, large and requires linguistic interpretation, here is where fuzzy logic stepped into this process [16], [17]. The application of fuzzy in solving decision problems has been successfully implemented by integrating fuzzy with non-tree-based decision algorithms [18], [19]. Recent study by [20], shown that a new framework of pairing fuzzy discretization and random forest, a tree-based decision algorithm, has been proposed to enhance the classification accuracy of random forest classification algorithm.…”
Section: Introductionmentioning
confidence: 99%