An Adaptive Fusion Algorithm for Spam Detection

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.

Authors

• Congfu Xu
• Baojun Su
• Yunbiao Cheng
• Weike Pan
• Li Chen BSH Home Appliances Holding (China) Co. , Ltd

Keywords

• Prediction algorithms
• Unsolicited electronic mail
• Adaptation models
• Feature extraction
• Algorithm design and analysis
• Online services
• Adaptive Algorithm
• Fusion Algorithm
• Adaptive Fusion
• Social Media
• Support Vector Machine
• Misinformation
• Link Function
• Matrix Factorization
• Prediction Score
• Online Learning
• Single Class
• Black Hole
• Social Media Sites
• Online System
• Feature Engineering
• Learning Scores
• Email Service
• Spam Emails
• Punishment
• Learning Rate
• Ensemble Method
• Vector Space Model
• Fusion Approach
• Non-confrontational
• Number Of Learners
• Base Classifiers
• Final Prediction
• spam detection
• intelligent systems