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Issue No. 04 - July-Aug. (2014 vol. 29)
ISSN: 1541-1672
pp: 2-8
Congfu Xu , Zhejiang University
Baojun Su , Zhejiang University
Yunbiao Cheng , Zhejiang University
Weike Pan , Shenzhen University, Hong Kong Baptist University
Li Chen , Hong Kong Baptist University
Spam detection has become a critical component in various online systems such as email services, advertising engines, social media sites, and so on. Here, the authors use email services as an example, and present an adaptive fusion algorithm for spam detection (AFSD), which is a general, content-based approach and can be applied to nonemail spam detection tasks with little additional effort. The proposed algorithm uses n-grams of nontokenized text strings to represent an email, introduces a link function to convert the prediction scores of online learners to become more comparable, trains the online learners in a mistake-driven manner via thick thresholding to obtain highly competitive online learners, and designs update rules to adaptively integrate the online learners to capture different aspects of spams. The prediction performance of AFSD is studied on five public competition datasets and on one industry dataset, with the algorithm achieving significantly better results than several state-of-the-art approaches, including the champion solutions of the corresponding competitions.
Prediction algorithms, Unsolicited electronic mail, Adaptation models, Feature extraction, Algorithm design and analysis, Online services,intelligent systems, spam detection, adaptive fusion
Congfu Xu, Baojun Su, Yunbiao Cheng, Weike Pan, Li Chen, "An Adaptive Fusion Algorithm for Spam Detection", IEEE Intelligent Systems, vol. 29, no. , pp. 2-8, July-Aug. 2014, doi:10.1109/MIS.2013.54
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