Application of Decision Tree Methods on Spam Filtering

• Main Authors: Meng-Chuan, Tsai, 蔡孟娟
• Other Authors: Ching-Hsiang Chen
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/95491262592610440256

碩士 === 淡江大學 === 統計學系碩士班 === 93 === As a result of the progress on computer science and the development of Internet, Email has been the important communication medium in daily life. Email Advertising becomes the most efficient technique in marketing, and therefore arises the problem about spam. The amounts of spam increase quickly. It not only takes the network resources and makes the burden on system, but also wastes the receiver’s time. Spam filtering becomes a popular research issue in recent years. In this study, we use three decision tree methods of data mining technology to classify Emails into “spam” and “legitimate” based on fourteen characteristics of Email. The three decision tree methods are compared with bayes classifier, which is most often used in spam filtering at present. When the efficiency of classification and misclassification costs are considered, C4.5 method has the best outcome in our case study of spam mails. It takes the shortest test time among the three decision tree methods. Our study also shows that we can avoid misclassifying legitimate by using the white list before we apply the classification.