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

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dc.contributor.author Tran, TP
dc.contributor.author Tsai, PC
dc.contributor.author Jan, T
dc.contributor.editor NA
dc.date.accessioned 2010-05-28T09:59:05Z
dc.date.issued 2008-01
dc.identifier.citation 19th International Conference on Pattern Recognition, 2008, pp. 1 - 4
dc.identifier.issn 1051-4651
dc.identifier.other E1 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.
dc.publisher IEEE
dc.relation.isbasedon 10.1109/ICPR.2008.4761358
dc.title An adjustable combination of linear regression and modified probabilistic neural network for anti-spam filtering
dc.type Conference Proceeding
dc.parent 19th International Conference on Pattern Recognition
dc.journal.number en_US
dc.publocation Piscataway, USA en_US
dc.identifier.startpage 1 en_US
dc.identifier.endpage 4 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.conference International Conference on Pattern Recognition
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.for 080104 Computer Vision
dc.for 080109 Pattern Recognition and Data Mining
dc.for 080105 Expert Systems
dc.personcode 020524
dc.personcode 044177
dc.personcode 999525
dc.percentage 40 en_US
dc.classification.name Neural, Evolutionary and Fuzzy Computation en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom International Conference on Pattern Recognition en_US
dc.date.activity 20081208 en_US
dc.date.activity 2008-12-08
dc.location.activity Tampa Bay, USA en_US
dc.description.keywords classification computational complexity e-mail filters feature extraction information filtering neural nets probability regression analysis unsolicited e-mail en_US
dc.description.keywords classification computational complexity e-mail filters feature extraction information filtering neural nets probability regression analysis unsolicited e-mail
pubs.embargo.period Not known
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 Computing and Communications
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history School of Computing and Communications (ID: 335)
utslib.collection.history Closed (ID: 3)


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