Study of Hybrid Data Mining Techniques Applied for Filtering Spam Mail

• Main Authors: I-Ping Shu, 徐一平
• Other Authors: Zne-Jung Lee
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/00992363360301001047

碩士 === 華梵大學 === 資訊管理學系碩士班 === 97 === The network has been established and developed since 1970; people have generally used the network. People artificially delivered mail before, but this tendency was transferred to E-mail. The time and distance of communication were decreased by E-mail, and E-mail gradually changed our live and working way. At this moment, some beneficial people use the malicious programs or collect the email boxes in many ways, then send email arbitrarily. It has been perplexed to the receiver. This study (GA/DT) adopts the genetic algorithm (Genetic Algorithms, GA) and decision tree (Decision Tree, DT) of data mining techniques to select Minimum Case and Pruning CF parameters. Experiment results indicate that the accuracy of the hybrid GA/DT algorithm is 95.0%. In other algorithms, Logistic Algorithm has a better accuracy of 5.0% and ANN has an accuracy of 2.337 % and SVM has an accuracy of 4.0 %, and it shows that the GA/DT algorithm can accurately select the Minimum Case and Pruning CF parameters in the DT algorithm and effectively enhance the performance of identifying spam mails.