Adversarial Classification: An Ensemble-based approach

Master's Thesis Defense Announcement

Spam has been studied and dealt with extensively in the email, web, and, recently, blog domains. Recent work has addressed the problem of non-stationarity of the data using ensemble-based approaches. Adversarial classification has been handled by retraining base classifiers using labeled samples obtained from the ensemble. However, frequent retraining is expensive. There is a need is to dynamically determine when the classifiers should be retrained and to retrain only those classifiers that are performing poorly. We show how mutual agreement between classifiers can be use to reduce retraining time, measure runtime performance, and keep track of the weakest performing classifier(s). We back our research with experimental results using real life data from blogs as a special case of spam.
Committee Members

• Dr. Tim Oates (Chair)
• Dr. Tim Finin
• Dr. Charles Nicholas
• Dr. Pranam Kolari