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The Impact of Feature Selection on Web Spam Detection
2012
International Journal of Intelligent Systems and Applications
Signature-driven spam detection provides an alternative to machine learning approaches and can be very effective when near-duplicates of essentially the same message are sent in high volume [20] . Unfortunately, signatures can also be brittle to small alterations of message content. In this work we propose a technique for increasing signature robustness, targeting the I-Match algorithm [6] , but applicable to other single-signature detection schemes. The proposed method is shown to consistently
doi:10.5815/ijisa.2012.09.08
fatcat:fooj3rz3qbf25k7skwzxx4sohm