Shuangcheng Wang

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For learning a Bayesian network classifier, continuous attributes usually need to be discretized. But the discretization of continuous attributes may bring information missing, noise and less sensitivity to the changing of the attributes towards class variables. In this paper, we use the Gaussian kernel function with smoothing parameter to estimate the(More)
Naive Bayes Classifiers have been known with the advantages of high efficiency and good classification accuracy and they have been widely used in many domains. However, the classifiers need complete data. And the phenomenon of missing data widely exists in practice. Facing this instance, learning naive Bayes classifier and classification method with missing(More)
At present, the methods of risk warning emphasize static function dependency or dynamic propagation of time series so that the static and dynamic information can not be consistently combined. In this paper, a dynamic multi-hierarchical Bayesian network classifier is developed for warning operational risk. And an example is presented to explain the process(More)
Accurate prediction of operating conditions and effective reduction of operating risks leads to be an important and sticky issue to today's enterprise. This paper, using scenario analysis, constructs various conditions and future development path scenarios based on rigorous reasoning describe and makes forecasts by joint reasoning based on Bayesian Network(More)
Numerous data mining models have been proposed to construct computer-aided medical expert systems. Bayesian network classifiers (BNCs) are more distinct and understandable than other models. To graphically describe the dependency relationships among clinical variables for thyroid disease diagnosis and ensure the rationality of the diagnosis results, the(More)
Bayesian networks are graphical models that describe dependency relationships between variables, and are powerful tools for studying probability classifiers. At present, the causal Bayesian network learning method is used in constructing Bayesian network classifiers while the contribution of attribute to class is overlooked. In this paper, a Bayesian(More)
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