一种解决不平衡情感分类的EM改进算法

An Improved Algorithm of EM for Unbalanced Sentiment Classification

基于朴素贝叶斯模型的EM算法经常被应用到情感分类中,但是其存在自身的缺点,当训练样本的类别不平衡时,分类器会越来越偏向于某一类,导致结果变差。本文在EM算法的基础上提出了一种改进的算法,来解决这一问题,并且通过实验我们可以发现该算法要优于普通的EM算法,证明了该算法的有效性以及合理性。

EM algorithm based on naive bayesian model is often applied to the sentiment classification,but it has shortcomings.When the training sample categories are unbalanced,The classifier will be more and more tend to a certain category,which will lead to worse results.In this paper,on the basis of the EM algorithm an improved algorithm is proposed to solve this problem.And through the experiment we can find that the proposed algorithm is superior to ordinary EM algorithm.The experiment proves the validity and rationality of our algorithm.