Current Research Topics:
Past Research Topics: [+]
Description: We are looking at problems related to the generation of training data. We are interested in two scenarios. 1) A new class of problems we have defined, Active Class Selection (ACS). ACS addresses the question: if one can collect n additional instances, how should they be distributed with respect to class? 2) Active Learning, in which one requests labels for existing training data.
Specifically, Active Class Selection addresses the tasks for which one can control the classes from which training data are generated. In such cases, utilizing feedback during learning to guide the generation of new training data will yield better performance than learning from an a priori fixed class distribution. Our methods work within a multi-armed bandit framework.
In regard to active learning, we are investigating several real-world issues. Speficially, how to perform active learning in the context of severe class imbalance, how to adapt to changes in the underlying concept to be learned (concept drift), and how to inject domain knowledge into the AL framework.