%0 Journal Article
%T Classification algorithm for self-learning Naive Bayes based on conditional information entropy<br>基于条件信息熵的自主式朴素贝叶斯分类算法
%A DENG Wei-bin
%A HUANG Shu-jiang
%A ZHOU Yu-min
%A <br>邓维斌
%A 黄蜀江
%A 周玉敏
%J 计算机应用
%D 2007
%I 
%X Nave Bayes algorithm is an effective simple classification algorithm.But two central assumptions made by the Nave Bayes approach are that the attributes are independent within each class and the importance of the attributes is equal,which can harm the classification process to some extent.It is a very difficult problem in machine learning to carry out self-learning knowledge according to the characteristic of source data without prior domain knowledge.Based on the theory of rough set,a new Nave Bayes method named Conditional Information Entropy-based Algorithm for Self-learning Nave Bayes(CIEBASLNB)was proposed,which combined the merits of selective Nave Bayes(SNB)and Weighted Nave Bayes(WNB).Simulation results on a variety of UCI data sets illustrate the efficiency of this method.
%K Naive Bayes
%K rough set
%K conditional information entropy
%K self-learning
%K classification<br>朴素贝叶斯
%K 粗糙集
%K 条件信息熵
%K 自主式学习
%K 分类
%K 条件信息熵
%K 自主式
%K 朴素贝叶斯分类算法
%K algorithm
%K Classification
%K information
%K entropy
%K conditional
%K based
%K 有效性
%K 验证
%K 仿真实验
%K 数据集
%K 加权
%K 选择
%K 结合
%K 分类方法
%K 相关理论
%K Rough
%K 机器学习
%K 自主学习
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=831E194C147C78FAAFCC50BC7ADD1732&aid=392D838D03DB022E151397AAE91BE435&yid=A732AF04DDA03BB3&vid=DB817633AA4F79B9&iid=E158A972A605785F&sid=68BCD01D0D745EB3&eid=412FA1328E0CB9E9&journal_id=1001-9081&journal_name=计算机应用&referenced_num=8&reference_num=15