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Please use this identifier to cite or link to this item: http://ir.lib.stu.edu.tw:80/ir/handle/310903100/1946

Title: A Machine Learning Approach for Spam E-mail Filtering
A Machine Learning Approach for Spam E-mail Filtering
Authors: 羅天利
Khim, Chamroeun
Contributors: Shing-Hwang Doong
資訊管理研究所
Keywords: 貝式演算;向量機演算
Support Vector Machine;Naïve Bayesian
Date: 2007
Issue Date: 2011-05-26 11:07:06 (UTC+8)
Publisher: 高雄市:[樹德科技大學資訊管理研究所]
Abstract: NA
In the era of the Internet, electronic mail is an effective way of communication
between the people throughout the world. Taking advantages from the ease of the
popularity of E-mail, Spammers have been intentionally delivering a thousand of unwanted
e-mail to e-mail users a day. Therefore, spam addresses several issues for the Internet
society due to several negative effects on individual users, Internet Service Providers
(ISPs) and Organization.
As spamming is considered a serious threat for the digital age, the research into Email
spam filtering is an ongoing concern. Researchers have employed various strategies to
filter out spam from legitimate mails. With their much effort, they discovered some spam
filtering techniques with different effectiveness, depending on the feature extraction,
feature representation and algorithms they used. In this study, we consider two machine
learning approaches - Naïve Bayesian, and Support Vector Machine- to differentiate spam
and ham in terms of information gain for feature extraction and term frequency-inverse
document frequency for feature representation. Most importantly, the body of the message
is taken into account. The result of the study indicates that both algorithms can produce
high accuracy of classification. However, the SVM outperforms Naïve Bayesian.
Appears in Collections:[資訊管理系(所)] 博碩士論文

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