Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining concept-drifting data streams using ensemble classifiers
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Spam detection using web page content: a new battleground
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Comment spam detection by sequence mining
Proceedings of the fifth ACM international conference on Web search and data mining
Spam detection using Random Boost
Pattern Recognition Letters
Information Retrieval on the Blogosphere
Foundations and Trends in Information Retrieval
Hi-index | 0.00 |
The standard method for combating spam, either in email or on the web, is to train a classifier on manually labeled instances. As the spammers change their tactics, the performance of such classifiers tends to decrease over time. Gathering and labeling more data to periodically retrain the classifier is expensive. We present a method based on an ensemble of classifiers that can detect when its performance might be degrading and retrain itself, all without manual intervention. Experiments with a real-world dataset from the blog domain show that our methods can significantly reduce the number of times classifiers are retrained when compared to a fixed retraining schedule, and they maintain classification accuracy even in the absence of manually labeled examples.