%0 Journal Article
%T Bayesian Network Classifier Based on L1 Regularization<br>基于L1正则化的贝叶斯网络分类器
%A WANG Ying
%A WANG Hao
%A YU Kui
%A YAO Hong-liang
%A <br>王影
%A 王浩
%A 俞奎
%A 姚宏亮
%J 计算机科学
%D 2012
%I 
%X 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.
%K Bayesian network classificr
%K Lasso method
%K K2 algorithm
%K L1 regularization<br>贝叶斯网络分类器(BNC)
%K Lasso方法
%K K2算法
%K L1正则化
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=1A50AD49C0C949493916335586529331&yid=99E9153A83D4CB11&vid=7C3A4C1EE6A45749&iid=CA4FD0336C81A37A&sid=D46BA3D3D4B3C585&eid=3A0155B37D8FF829&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=24