计算机应用研究2016，Vol.33Issue(5)：1335-1337,1358,4.DOI:10.3969/j.issn.1001-3695.2016.05.012
利用类概率估计的加权平均树增强朴素贝叶斯网络结构
Improving weighted averaged tree naive Bayes based on class probability estimation
摘要
Abstract
Tree augmented naive Bayes (TAN)improves naive Bayes (NB)by weakening its conditional attribute indepen-dence assumption,while maintaining efficiency and simplicity.In many real-world applications,however,classification accuracy or error rate of TAN was not enough.Thus,this paper investigated weighted averaged tree naive Bayes(ATAN)algorithm to im-prove its class probability estimation performance.And it estimated performance of ATAN in terms of log conditional likelihood (LCL).Meanwhile it applied the algorithm of the training and testing to estimate the performance.The experiments were done on a large number of UCI datasets published on the main Web site of Weka platform by using the methods of cross validation. The results show that ATAN significantly outperforms TAN and ONB all the other algorithms used to compare in terms of LCL.关键词
加权平均树增强朴素贝叶斯/分类概率估计/对数条件似然/网络结构Key words
weighted averaged tree naive Bayes/class probability estimation/log conditional likelihood/network structure分类
信息技术与安全科学引用本文复制引用
丁一,周海磊,林国龙..利用类概率估计的加权平均树增强朴素贝叶斯网络结构[J].计算机应用研究,2016,33(5):1335-1337,1358,4.基金项目
国家自然科学基金资助项目 （）
