Network Security White Papers
Web Spam Challenge 2007 Track II Secure Computing Corporation Research
Overview To discriminate spam Web hosts/pages from normal ones, text-based and link-based data are provided for Web Spam Challenge Track II. Given a small part of labeled nodes (about 10%) in a Web linkage graph, the challenge is to predict other nodes' class to be spam or normal. This paper extracts features from link-based data, and then combines them with text-based features. After feature scaling, Support Vector Machines (SVM) and Random Forests (RF) are modeled in the extremely high dimensional space with about 5 million features. Stratified 3-fold cross validation for SVM and out-of-bag estimation for RF is used to tune the modeling parameters and estimate the generalization capability. On the small corpus for Web host classification, the best F-Measure value is 75.46% and the best AUC value is 95.11%.
| Publisher | Secure Computing | File Format | |
|---|---|---|---|
| Date Published | August 2007 | ||
| Format | White Papers | ||
| Topics | |||
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