基于不完全朴素贝叶斯分类模型的垃圾邮件分类模型

Anti-spam model based on semi-Naive Bayesian classification model

由于朴素贝叶斯分类模型的简单高效,在垃圾邮件分类时可以达到较好的效果;但朴素贝叶斯的条件独立假设割裂了属性之间的关系,影响了分类的准确性。放松朴素贝叶斯分类模型关于属性之间条件独立假设,介绍一种新的基于不完全朴素贝叶斯分类模型的垃圾邮件分类模型,N平均1-依赖邮件过滤模型。使用N个1-依赖分类模型的平均概率作为分类的预测概率。实验证明,该模型在简单、高效的同时降低了对垃圾邮件分类的错误率。

Because Naive Bayes(NB) classification model is simple and effective,good efficiency can be achieved in anti-spam applications.On the other hand,the assumption of its attribute independence makes it unable to express its semantic dependence.This paper proposed a new anti-spam classification model based on semi-NB classification model,averaged on N one-dependence classification model.It relaxed the assumption of condition independence of each attribute.It was assumed that all attributes were dependent on one...