Bayesian Network Classifier Based on L1 Regularization
Hongliang Yao · 2012
Variable order-based Bayesian network classifiers ignore the information of the selected variables in their sequence and their class label,which significantly hurts the classification accuracy.To address this problem,we proposed a simple and efficient L1 regularized Bayesian network classifier(L1-BNC).Through adjusting the constraint value of Lasso and fully taking advantage of the regression residuals of the information,L1-BNC takes the information of the sequence of selected variables and the class label into account,and then generates an excellent variable ordering sequence(L1 regularization path) for constructing a good Bayesian network classifier by the K2 algorithm.Experimental results show that L1-BNC outperforms existing state-of-the-art Bayesian network classifiers.In addition,in comparison with SVM,Knn and J48 classification algorithms,L1-BNC is also superior to those algorithms on most datasets.