互信息匹配的半朴素贝叶斯分类器

由于作为朴素贝叶斯分类器的主要特征的条件独立性假设条件过强且在不同数据集上表现出的差异，所以独立性假设成为众多改进算法的切入点。但也有研究指出不满足该假设并没有对分类器造成预想的影响。从降低后验概率的估计误差入手提出一种条件熵匹配的半朴素贝叶斯分类器。实验证明，该方法能有效提高朴素贝叶斯分类器的性能。

Semi-naive Bayesian classifier matched by mutual information

Because the class-conditional independence assumption which is the mainly feature of Naive Bayesian classifier is a so strong assumption and difference appears between datasets, the class-conditional independence assumption becomes the entry point of improvement methods. But some researches indicate that the violations of independence assumption do not make so much influence to the classifier as expected. This paper proposes a conditional entropy matching half-naive Bayesian classifier for the purpose of lower posterior probability estimation error. Experiments show that this method can effectively improve the performance of naive Bayesian classifier.