计算机科学 ›› 2014, Vol. 41 ›› Issue (6): 214-216.doi: 10.11896/j.issn.1002-137X.2014.06.042
翟军昌,秦玉平,车伟伟
ZHAI Jun-chang,QIN Yu-ping and CHE Wei-wei
摘要： 针对垃圾邮件过滤中的特征项选择问题,提出了一种改进的信息增益方法。首先利用特征词的先验概率定义增益比,然后利用增益比对特征词为整个分类所提供的信息量进行放大或弱化,从而对特征词的类别条件熵计算作了改进,采用极大后验假设朴素贝叶斯决策方法在英文语料库上进行实验,通过召回率、正确率、精确率和错误率对算法进行评价分析。实验结果表明,改进后的算法提高了过滤器的分类精度,降低了过滤器对合法邮件的误判给用户带来的损失。
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