Link Based Small Sample Learning for Web Spam DetectionGuang-GangGengauthorQiudanLiauthorXinchangZhangauthorRobust 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.
2009-04Conference or Workshop Item