Spam E-mail Classification Based on the IFWB Algorithm

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题名:	Spam E-mail Classification Based on the IFWB Algorithm
作者:	Jou, Chichang
贡献者:	淡江大學資訊管理學系
日期:	2013-03-19
上传时间:	2015-04-13 16:21:08 (UTC+8)
出版者:	Springer
摘要:	The problem of spam e-mails has been addressed for some time. Most of the solutions are based on spam e-mail classification and filtering. However, the content of spam e-mails drifts with new concepts or social events. Thus, several spam classifiers perform effectively when their models are initially established, and their performances deteriorate with time. A learning mechanism is required to adjust the classification parameters for new and old e-mails. Because of the spread of spam e-mails, the number of spam e-mails is larger than that of legitimate e-mails. Therefore, most classifiers produce high recall for spam e-mails and low recall for legitimate e-mails. Based on the Bayesian algorithm, we propose an incremental forgetting weighted algorithm with a misclassification cost mechanism that extracts features by IGICF (Information Gain and Inverse Class Frequency) to address the problem of concept drift and data skew in spam e-mail classification. We implemented the algorithm and performed detailed tests on the effectiveness of the mechanism.
關聯:	Lecture Notes in Computer Science 7802, pp.314-324
显示于类别:	[資訊管理學系暨研究所] 會議論文

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