Link Based Small Sample Learning for Web Spam Detection

Presented at: 18th International World Wide Web Conference (WWW2009)

by Guang-Gang Geng, Qiudan Li, Xinchang Zhang

Webpage: http://www2009.eprints.org/173/1/p1185.pdf

Robust statistical learning based web spam detection sys- tem often requires large amounts of labeled training data. However, labeled samples are more difficult, expensive and time consuming to obtain than unlabeled ones. This pa- per 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.

Keywords: Poster Session


Resource URI on the dog food server: http://data.semanticweb.org/conference/www/2009/paper/173


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