An E-mail Filtering Approach Using Neural Network

@inproceedings{Cao04, abstract = {The communication via electronic mail is one of the most popular services of Internet. The volume of emails that we get is constantly growing. In particular, unsolicited messages or spam, flood our email boxes, and result in causing frustration, and wasting bandwidth and time. The paper presents a novel schema to automatically filter spam emails by using the principal component analysis(PCA) and the Self Organized Feature Map (SOFM). In our schema, each email is represented by a series of textual and non-textual features. To reduce the number of textual features, PCA is used to select the most relevant features. Finally the output of the PCA and the non-textual features should be inputted into a well-trained SOFM to classify ( spam or normal). In comparison with some traditional classification methods, the experimental result denotes that the scheme will increase the accuracy of filtering emails.}, author = {Cao, Yukun and Liao, Xiaofeng and Li, Yunfeng }, booktitle = {Advances in Neural Networks - ISNN 2004}, citeulike-article-id = {3044862}, journal = {Advances in Neural Networks - ISNN 2004}, keywords = {cas\_chapter, email, security, som, spam}, pages = {688--694}, posted-at = {2008-07-26 19:04:20}, priority = {5}, series = {LNCS}, title = {An E-mail Filtering Approach Using Neural Network}, url = {http://www.springerlink.com/content/ynep3pq482c95l8d }, year = {2004} }

TY - CONF ID - Cao04 L3 - citeulike-article-id:3044862 TI - An E-mail Filtering Approach Using Neural Network T2 - Advances in Neural Networks - ISNN 2004 SE - LNCS JF - Advances in Neural Networks - ISNN 2004 SP - 688 EP - 694 N2 - The communication via electronic mail is one of the most popular services of Internet. The volume of emails that we get is constantly growing. In particular, unsolicited messages or spam, flood our email boxes, and result in causing frustration, and wasting bandwidth and time. The paper presents a novel schema to automatically filter spam emails by using the principal component analysis(PCA) and the Self Organized Feature Map (SOFM). In our schema, each email is represented by a series of textual and non-textual features. To reduce the number of textual features, PCA is used to select the most relevant features. Finally the output of the PCA and the non-textual features should be inputted into a well-trained SOFM to classify ( spam or normal). In comparison with some traditional classification methods, the experimental result denotes that the scheme will increase the accuracy of filtering emails. KW - cas_chapter KW - email KW - security KW - som KW - spam AU - Cao, Yukun AU - Liao, Xiaofeng AU - Li, Yunfeng PY - 2004/// UR - http://www.springerlink.com/content/ynep3pq482c95l8d ER -