【摘要】 为了降低朴素贝叶斯分类模型的独立性假设约束,提出一种混合式朴素贝叶斯分类模型(MBN:Mixed Naive Bayes)。通过分析贝叶斯定理,把条件属性集合划分成若干个独立的属性子集,用树增广朴素贝叶斯分类对属性子集分别进行分类学习,通过公式进行整合。将该模型算法与朴素贝叶斯及树增广朴素贝叶斯进行实验比较,实验结果表明MBN分类器在多数数据集上具有较高的分类正确率。更多还原

【Abstract】 In order to decrease the attribute independence assumption which is made by Naive Bayesian,a new Bayesian model MBN(Mixed Naive Bayes) is introduced.It divides attribute sets into several independent subsets by analyzing Bayesian theorem. The subsets are trained by TAN(Tree Augmented Naive Bayes) and the results are integrated by formula.MBN classifier is compared with Naive Bayes classifier and TAN classifier by an experiment.Experimental results show that this model has higher classification accuracy in most data sets.更多还原