Conditional Bayesian network classifier and its application in product failure rate grade indentifying

Aiming at the weakness of traditional Bayesian network classifiers, a new kind of classifier model based on Conditional Bayesian Networks (CBN) was proposed. With the indication of the conditional independence relationship among attribute variables given the target variable, this model provided an effective approach for classification problems. Based on this, the modeling method for building CBN classifier was listed to guiding the modeling and application. Case study was carried out and the results showed that, comparing to existing Bayesian networks classifiers and traditional decision tree C4. 5, the CBN not only enhanced the total precision but also reduced the complexity of network structure.