Department of Computer Science
An adaptive fusion algorithm for spam detection
© 2001-2011 IEEE. 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.
adaptive fusion, intelligent systems, spam detection
Source Publication Title
IEEE Intelligent Systems
Institute of Electrical and Electronics Engineers
Link to Publisher's Edition
Xu, Congfu, Baojun Su, Yunbiao Cheng, Weike Pan, and Li Chen. "An adaptive fusion algorithm for spam detection." IEEE Intelligent Systems 29.4 (2014): 2-8.