Bayesian Network Classifier Based on L1 Regularization
基于L1正则化的贝叶斯网络分类器

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 Ll regularized I3ayesian network classifier (Ll-I3NC). 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.