Lazy Associative Classification
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Automatic vandalism detection in Wikipedia
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Modern Information Retrieval
Crowdsourcing a wikipedia vandalism corpus
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Rule-based active sampling for learning to rank
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Proceedings of the 7th International Symposium on Wikis and Open Collaboration
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Wikipedia and other free editing services for collaboratively generated content have quickly grown in popularity. However, the lack of editing control has made these services vulnerable to various types of malicious actions such as vandalism. State-of-the-art vandalism detection methods are based on supervised techniques, thus relying on the availability of large and representative training collections. Building such collections, often with the help of crowdsourcing, is very costly due to a natural skew towards very few vandalism examples in the available data as well as dynamic patterns. Aiming at reducing the cost of building such collections, we present a new active sampling technique coupled with an on-demand associative classification algorithm for Wikipedia vandalism detection. We show that our classifier enhanced with a simple undersampling technique for building the training set outperforms state-of-the-art classifiers such as SVMs and kNNs. Furthermore, by applying active sampling, we are able to reduce the need for training in almost 96% with only a small impact on detection results.