基于知识积累方法的垃圾邮件过滤算法

由于垃圾邮件类型的不断变化，超出现有过滤算法模型中所采用的训练样本范围，造成过滤性能不断下降。因此，提出了一种基于知识积累方法的垃圾邮件过滤算法。利用KNN方法的惰性学习机制，将新型的垃圾邮件动态加入训练样本集中，以实现知识的不断积累过程。在此基础上，应用KNN方法对未知邮件样本进行精确匹配，以排除合法邮件的误判结果。实验表明算法具有较好的过滤性能。

Spam Filtered Algorithm Based on Knowledge Accumulation

New variation of spam leads to exceed range of training set used in current spam filtering algorithms, and lower their performance of filtering illegal emails. To solve this problem, a new algorithm is provided based on knowledge accumulation. Using the laziness of KNN(K nearest neighbors), it automatically expands the training set with the help of new spam, and then uses KNN to classify the unlabelled emails. Experimental results also show the new algorithm with better performance on filtering spam.