The dependency extension of naive Bayes classifiers with continuous attributes
Wang Li · Journal of Northeast Normal University · 2012
On account of naive Bayesian classifiers can't make good use of the dependence information between attribute variables.We extend naive Bayesian classifiers with continuous attributes using tree-like graphical models.The method based on computation of mutual information of the continuous attributes and conditional density,combining the construction of parent node selection of the class-constrained attribute maximum weighted spanning tree.Comparative experiments and analysis are done.Experimental results show that classification accuracy of the extended naive Bayesian classifier with continuous attributes has improved obviously.