An adaptive fusion algorithm for spam detection

Congfu Xu*, Baojun Su, Yunbiao Cheng, Weike Pan, Li CHEN

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Article number6563073
Pages (from-to)2-8
Number of pages7
JournalIEEE Intelligent Systems
Issue number4
Publication statusPublished - 1 Jul 2014

Scopus Subject Areas

  • Computer Networks and Communications
  • Artificial Intelligence

User-Defined Keywords

  • adaptive fusion
  • intelligent systems
  • spam detection


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