An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering

Tran, TP; Tsai, P; Jan, T

An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering

Tran, TP Tsai, P Jan, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - International Conference on Pattern Recognition, 2008
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Tran, TP	en_US
dc.contributor.author	Tsai, P	en_US
dc.contributor.author	Jan, T	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Proceedings - International Conference on Pattern Recognition, 2008	en_US
dc.identifier.isbn	9781424421756	en_US
dc.identifier.issn	1051-4651	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10720
dc.description.abstract	Email is a commonly used tool for communication which allows rapid and asynchronous communication. The growing popularity and low cost of e-mails have made spamming an extremely serious problem today. Several anti-spam filtering techniques have been developed but most of them suffer from low accuracy and high false alarm rate due to complexity and changing nature of unsolicited messages. This study proposes an innovative classification framework with comparable accuracy, affordable computation and high system robustness. In particular, an effective feature selection scheme is implemented in conjunction with an adjustable combination of linear and nonlinear learning algorithms. Extensive experiments have indicated that the proposed framework compares favorably to other state-of-the-art methods, especially when misclassification cost is high. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - International Conference on Pattern Recognition	en_US
dc.title	An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
utslib.for	080104 Computer Vision	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	080105 Expert Systems	en_US
dc.location.activity	Tampa Bay, USA	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Email is a commonly used tool for communication which allows rapid and asynchronous communication. The growing popularity and low cost of e-mails have made spamming an extremely serious problem today. Several anti-spam filtering techniques have been developed but most of them suffer from low accuracy and high false alarm rate due to complexity and changing nature of unsolicited messages. This study proposes an innovative classification framework with comparable accuracy, affordable computation and high system robustness. In particular, an effective feature selection scheme is implemented in conjunction with an adjustable combination of linear and nonlinear learning algorithms. Extensive experiments have indicated that the proposed framework compares favorably to other state-of-the-art methods, especially when misclassification cost is high. © 2008 IEEE.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/10720