基于二维信息增益加权的朴素贝叶斯分类算法

Naive Bayes Classification Algorithm of Feature Weighting Based on Two-Dimensional Information Gain

由于朴素贝叶斯算法的特征独立性假设以及传统TFIDF加权算法仅仅考虑了特征在整个训练集的分布情况,忽略了特征与类别和文档之间关系,造成传统方法赋予特征的权重并不能代表其准确性.针对以上问题,提出了二维信息增益加权的朴素贝叶斯分类算法,进一步考虑到了特征的二维信息增益即特征类别信息增益和特征文档信息增益对分类效果的影响,并设计实验与传统的加权朴素贝叶斯算法相比,该算法在查准率、召回率、F1值指标性能上能提升6%左右.

Naive Bayes algorithm is based on feature-independence assumption and the traditional TF-IDF weighting algorithm, and only considers the distribution of features in the whole training set, but ignores the relationship between feature and categories or documents, so the weights given by traditional method cannot represent its performance. To solve the above problems, this study proposes a naive Bayes classification algorithm of feature weighting based on twodimensional information gain. It considers the effects of two-dimensional information gain of features, which are the information gain of category and the information gain of documents. Compared with the traditional naive Bayesian algorithm of feature weighting, the proposed algorithm can improve about 6% in the precision, recall, F1 value performance.