A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Challenges in web search engines
ACM SIGIR Forum
Mining with rarity: a unifying framework
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Detecting spam web pages through content analysis
Proceedings of the 15th international conference on World Wide Web
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Improving web spam classifiers using link structure
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Web spam detection via commercial intent analysis
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Boosting the Performance of Web Spam Detection with Ensemble Under-Sampling Classification
FSKD '07 Proceedings of the Fourth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 04
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Evaluating Arabic spam classifiers using link analysis
Proceedings of the 3rd International Conference on Information and Communication Systems
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Spamdexing is any of various methods to manipulate the relevancy or prominence of resources indexed by a search engine, usually in a manner inconsistent with the purpose of the indexing system. Combating Spamdexing has become one of the top challenges for web search. Machine learning based methods have shown their superiority for being easy to adapt to newly developed spam techniques. In this paper, we propose a two-stage classification strategy to detect web spam, which is based on the predicted spamicity of learning algorithms and hyperlink propagation. Preliminary experiments on standard WEBSPAM- UK2006 benchmark show that the two-stage strategy is reasonable and effective.