Link based small sample learning for web spam detection

Robust statistical learning based web spam detection system often requires large amounts of labeled training data. However, labeled samples are more difficult, expensive and time consuming to obtain than unlabeled ones. This paper proposed link based semi-supervised learning algorithms to boost the performance of a classifier, which integrates the traditional Self-training with the topological dependency based link learning. The experiments with a few labeled samples on standard WEBSPAM-UK2006 benchmark showed that the algorithms are effective. Categories and Subject Descriptors H.5.4 [Information Interfaces and Presentation]: Hypertext/Hypermedia; K.4.m [Computer and Society]: Miscellaneous; H.4.m [Information Systems]: Miscellaneous General Terms Measurement, Experimentation, Algorithms Keywords Link spam, Content spam, Web spam, Machine learning

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Guanggang Geng, Qiudan Li, Xinchang Zhang
• Keywords: Internet Technology, Keywords Link Spam, Semi-supervised Learning Algorithms, Web Spam Detection, WWW 2009