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《Computer Engineering and Design》 2011-11
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Naive Bayesian classifier based on factor analysis and it’s application to slope recognition

GAO Yan(School of Science,South China University of Technology,Guangzhou 510640,China)  
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.
【Fund】: 国家自然科学基金项目(61070033);; 广东省自然科学基金重点项目(9251009001000005);; 广东省科技计划基金项目(2010B050400011、2008B080701005);; 广东省哲学社会科学规划“十一五”规划基金项目(08O-01)
【CateGory Index】: TP311.13
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