计算机工程与应用2017，Vol.53Issue(5)：69-72,4.DOI:10.3778/j.issn.1002-8331.1507-0161
用平滑方法改进多关系朴素贝叶斯分类
Improving multi-relational Naive Bayesian classifier using smoothing methods
摘要
Abstract
To eliminate the naive Bayesian classification of zero probability and overfitting problem, this paper discusses the various probability smoothing method, gives MRNBC-M(Multi-Relational Naive Bayesian Classifier based on M-estimation)and MRNBC-L(Multi-Relational Naive Bayesian Classifier based on Laplace-estimation). In the case of multi-relationship, the impact of M and Laplace estimation methods on the classification is analyzed. In order to further optimize the classification, the method is based on the extended mutual information criterion. Experiments on the multi-relational datasets show that MRNBC-M can effectively improve the classification performance.关键词
多关系数据挖掘/朴素贝叶斯/参数平滑/互信息Key words
Multi-Relational Data Mining(MRDM)/Naive Bayes/smoothing methods/mutual information分类
信息技术与安全科学引用本文复制引用
徐光美,刘宏哲,张敬尊,王金华..用平滑方法改进多关系朴素贝叶斯分类[J].计算机工程与应用,2017,53(5):69-72,4.基金项目
国家自然科学基金(No.61372148,No.61202245) （No.61372148,No.61202245）
北京市"长城学者"计划项目(No.CIT&TCD20130320) （No.CIT&TCD20130320）
北京市优秀人才培养项目(No.2010D005022000011) （No.2010D005022000011）
北京联合大学自然科学项目(No.zk20201403). （No.zk20201403）
