Web Spam Detection with Feature Fusion

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Provided by: Binary Information Press
Topic: Data Management
Format: PDF
Feature selection is one of the key factors that influences the development of statistical learning based web spam detection system. In this paper, except for content features and page-level link analysis features, the authors further extract host-level link analysis features. The effectiveness of the aforementioned features is analyzed on WEBSPAM-UK2006 benchmark. Experiments show that the features of different perspectives have different identification ability and provide great complement to each other. With fused features, the best detection performance is achieved.
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