一种基于信息增益的新垃圾邮件特征选择算法

基于传统信息增益特征选择算法,通过提出类内分散度与类间集中度的概念,结合传统信息增益算法,解决了信息增益算法因忽略特征项的分布而导致的性能下降问题,提高了信息增益算法的效率.使用改进的特征选择算法进行垃圾邮件过滤实验,在不同的分类器下,与传统的特征选择算法进行对比,实验结果表明,改进的特征选择算法性能较优.

A New Spam Feature Selection Algorithm Based on Information Gain

The concept of intra class dispersity and inter class concentration was proposed based on the traditional information gain feature se lection algorithm. Combined with the traditional information gain algorithm, i t solved the problem of performance degradation caused by ignoring the distribut ion of the characteristic items and improved the efficiency of the information g ain algorithm. The improved feature selection algorithm was applied to the spam filtering experiment. Compared with the traditional feature selection algorithms under different classifiers, the experimental results show that the improved fe ature selection algorithm has better performance.