Naive Bayesian classifier based on factor analysis and it’s application to slope recognition

Naive Bayesian classifier(NB) is popular for its simplicity and effectiveness.However,the accuracy of NB is affected when the conditional independence assumption is violated.A new algorithm based on factor analysis,FA-NBC,is proposed to retain the structure strength of NB while reducing error by alleviating the attribute interdependence problem.Then the classifier is applied to slope recognition.New independent attribute set which includes most of the information of the original property set is built based on varia-nce to ensure the structural simplicity of naive Bayesian classifier.Experimental results on UCI data sets prove the validity of the model.