%0 Journal Article
%T Research of P2P Traffic Identification Based on Decision Tree Ensemble<br>基于决策树集成的P2P流量识别研究
%A LIU San-min
%A SUN Zhi-xin
%A LIU Yu-xia
%A <br>刘三民
%A 孙知信
%A 刘余霞
%J 计算机科学
%D 2011
%I 
%X A novel P2P traffic identification method based on decision tree ensemble was proposed for improving the model stability. First, the most optimal feature set was extracted by using fast correlation based filter(FCBF) , and then the decision model based on five sub-classifier formed by Bagging was developed by the principle of the majority.Through test result comparison based on the open data set in the two distinct experiment scheme among the proposed model, naive bayes and naiva bayes based on kernel density estimation, it shows the proposed model owns a better stability, the high classification accuracy and P2P traffic identification accuracy and gives the explanation about this phenorEcnon.
%K Traffic identification
%K Ensemble learning
%K Decision tree
%K Bayes classification
%K Stabihty<br>流量识别，集成学习，决策树，贝叶斯分类，稳定性
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=17DDCED190714E79A61E6F359BB71932&yid=9377ED8094509821&vid=16D8618C6164A3ED&iid=708DD6B15D2464E8&sid=96C778EE049EE47D&eid=771469D9D58C34FF&journal_id=1002-137X&journal_name=计算机科学&referenced_num=0&reference_num=0