【摘要】 为进一步提高多关系朴素贝叶斯方法的分类准确率,分析了已有的剪枝方法,并扩展互信息标准到多关系情况下.基于元组号传播方法和面向元组的统计计数方法,给出了基于扩展互信息标准进行属性选择的方法和步骤,并建立了一种基于扩展互信息的多关系朴素贝叶斯分类器.标准数据集上的实验显示,基于扩展互信息标准进行属性选择,可以在不增加算法时间复杂度的前提下,找到与分类属性最相关的属性,并在仅有极少属性参与分类时,得到较高的分类准确率.Mutagenesis数据集上的实验则显示,这种属性选择可以使多关系问题退化为单关系问题,大大降低了分类代价.更多还原

【Abstract】 To improve the accuracy of multi-relational Nave Bayesian classifiers,the existing pruning methods were discussed and the attribute filter criterion was upgraded based on mutual information to deal with multi-relational data directly.On the basis of the tuple ID propagation method and counting methods towards tuple,the filter method based on extended mutual information was given,and a multi-relational Nave Bayesian classifier based on mutual information(MI-MRNBC) was implemented.Experimental results show that,in a multi-relational domain,with the help of the attribute filter based on extended mutual information,the classifier can give a better accuracy without the increase of time complexity.In extraordinary instances,the multi-relational classification degenerates into a single relational one,which extremely decreases the cost of classification.更多还原