基于信息增益的混合垃圾邮件特征选择方法  

Mixed spam feature selection approach based on information gain

作　　者：闫巧[1] 冷成朝[2] 

机构地区：[1]深圳大学计算机与软件学院,广东深圳518060 [2]深圳大学信息工程学院,广东深圳518060

出　　处：《计算机工程与应用》2012年第27期90-93,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(No.60972011)

摘　　要：特征选择是邮件过滤重要的环节,特征的好坏不仅影响分类的准确率,还直接影响到分类器训练和分类的开销。比较了常用的CHI选择、互信息(MI)、信息增益(IG)和SVM特征选择算法在垃圾邮件过滤中的效果,针对这些方法只排序而未消除特征间冗余的缺点,提出了利用特征词间条件概率和分类区分度消除冗余的混合邮件特征选择方法。实验结果表明:方法效果良好,提高了邮件分类准确率。Feature selection is a crucial process of spam filtering. The result of feature selection not only affects the accuracy of classification, but also affects the computational burden. The popular feature selection methods such as CHI selection, information gain, mutual information and SVM feature selection are compared and a mixed email feature selection method is proposed based on information gain using the conditional probability and classification discrimination between features to rudce redundancy among features to overcome their shortcoming that only pay at- tention to sorting yet ignore the redundancy among features. Experimental results show that： the new method is promising and improves classification accuracy of spam.

关 键 词：特征选择 卡方检验(CHI) 信息增益(IG) 支持向量机(SVM) 

分 类 号：TP311.56[自动化与计算机技术—计算机软件与理论]