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• [P86] A SMILE Web-based Interface for Learning the Causal Structure and Performing a Diagnosis of a Bayesian Network
  Author(s): Nipat Jongsawat, Wichian Premchaiswadi
  
  

  Abstract
  Bayesian networks (BNs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. The construction of BNs with the help of human experts is a difficult and time consuming task, which is prone to errors and omissions especially when the problems are very complicated. Learning the structure of a Bayesian network model and causal relations from a dataset or database is important for large BNs analysis. This paper focuses on using a SMILE web-based interface for building the structure of BN models from a dataset by using different structural learning algorithms. In addition to building the structure of BN models, a SMILE web-based interface also provides the feature set of Bayesian diagnosis for the user. The web application uses a novel user-friendly interface which intertwines the steps in the data analysis with brief support instructions to the Bayesian approach adopted. A SMILE web-based interface has been developed based on SMILE (Structural Modeling, Interface, and Learning Engine), SMILEarn, and SMILE.NET wrapper.

  
   

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