【摘要】 通过分析属性相关性的度量和贝叶斯定理的变形公式,研究基于强属性限定的贝叶斯分类模型SANBC和基于多维属性限定的贝叶斯分类模型MANBC,通过添加增强弧的方式表示属性间的依赖关系,弱化了朴素贝叶斯的独立性假设,扩展了朴素贝叶斯分类模型的结构.实验结果表明,与朴素贝叶斯分类模型相比,两种分类模型均具有较高的分类正确率.更多还原

【Abstract】 This paper studies SANBC(A Restricted Bayesian Classification Model Based on Strong Attributes) and MANBC(A Restricted Bayesian Classification Model Based on Multidimensional Attributes) through the analysis of a variant of Bayes theorem,the evaluation of condition attribute with correlation,following the extension of structure of Naive Bayesian classification Model,and the attribute independence assumption that Naive Bayesian classification Model can be weakened through the adding of highlighting lines among attributes.The experimental results show that,compared with Bayesian Classification Model,both the classification models have higher accuracy.更多还原