%0 Journal Article
%T Weighted Naive Bayes Classification Algorithm Based on Rough Set<br>基于Rough Set的加权朴素贝叶斯分类算法
%A DENG Wei-Bin
%A WANG Guo-Yin
%A WANG Yan
%A <br>邓维斌
%A 王国胤
%A 王燕
%J 计算机科学
%D 2007
%I 
%X Naive Bayes algorithm is an effective simple classification algorithm.Since its conditional independence assumption is not always true in real life,its classification performance is affected to some extent.Weighted naive Bayes(simply WNB)is an extension of it.Based on the attributes' importance degree theory of rough set,a new weighted naive Bayes method is proposed.Methods for determining the weights of attributes in the algebra view,informational view and both of them are developed respectively.Simulation results on a variety of UCI data sets illustrate the efficiency of this method.
%K Naivie bayes
%K Weighted naive bayes
%K Rough set
%K Weightiness of attribute
%K Classification<br>朴素贝叶斯
%K 加权朴素贝叶斯
%K Rough集
%K 属性重要性
%K 分类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=64A12D73428C8B8DBFB978D04DFEB3C1&aid=9498CA1B1A7D7F16A441ECBA33E92AF3&yid=A732AF04DDA03BB3&vid=339D79302DF62549&iid=0B39A22176CE99FB&sid=02DC3A182A5530DF&eid=9F6DA927E843CD50&journal_id=1002-137X&journal_name=计算机科学&referenced_num=6&reference_num=15