中文摘要：

文本分类是信息检索和文本挖掘的重要基础,朴素贝叶斯是一种简单而高效的分类算法,可以应用于文本分类.但是其属性独立性和属性重要性相等的假设并不符合客观实际,这也影响了它的分类效果.如何克服这种假设,进一步提高其分类效果是朴素贝叶斯文本分类算法的一个难题.根据文本分类的特点,基于文本互信息的相关理论,提出了基于互信息的特征项加权朴素贝叶斯文本分类方法,该方法使用互信息对不同类别中的特征项进行分别赋权,部分消除了假设对分类效果的影响.通过在UCIKDD数据集上的仿真实验,验证了该方法的有效性.

英文摘要：

Text classification is the foundation of information retrieval and text mining. Naive Bayes can be applied to text classification as it is simple and efficient classification. But the two assumption about Naive Bayes algorithm that its attribute independence is equal to its attribute importance are not always consistent with the reality, which also affects the classification results. It is a difficult problem to disapprove the assumptions and improve the classification effect.According to the characteristics of text classification, based on text mutual information theory, a Term Weighted Naive Bayes text classification method based on mutual information is proposed, which uses the mutual information method to weight the feature in different class. The effect of two assumptions on classification is partially eliminated. The effectiveness of the proposed method is verified by the simulation experiment on the UCI KDD data set.