This item is a Poster.
- Geng, Guang-Gang - Chinese Academy of Sciences
- Li, Qiudan - Chinese Academy of Sciences
- Zhang, Xinchang - Chinese Academy of Sciences
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Abstract
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.
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