Seminar Type: Invited Speakers
Title: An Alternative Approach to Designing Clinical Trials: Budgeted Learning of Effective Classifier
Start Time: 03/19/2010 - 11:00
End Time: 03/19/2010 - 12:00
Location: ICT 516
Speaker: Russel Greiner
Abstract:
Researchers often use clinical trials to collect the data needed to evaluate some hypothesis, or produce a classifier. During this process, they have to pay the cost of performing each test. Many studies will run a comprehensive battery of tests on each subject, for as many subjects as their budget will allow -- ie, "round robin" (RR). We consider a more general model, where the researcher can sequentially decide which single test to perform on which specific individual; again subject to spending only the available funds. Our goal here is to use these funds most effectively, to collect the data that allows us to learn the most accurate classifier.
We first explore the simplified "coins version" of this task. After observing that this is NP-hard, we consider a range of heuristic algorithms, both standard and novel, and observe that our "biased robin" approach is both efficient and much more effective than most other approaches, including the standard RR approach. We then apply these ideas to learning a naive-bayes classifier, and see similar behavior. Finally, we consider the most realistic model, where both the researcher gathering data to build the classifier, and the user (eg, physician) applying this classifier to an instance (patient) must pay for the features used --- eg, the researcher has $10,000 to acquire the feature values needed to produce an optimal $30/patient classifier. Again, we see that our novel approaches are almost always much more effective that the standard RR model.
This is joint work with Aloak Kapoor, Dan Lizotte and Omid Madani.
Biography:After earning a PhD from Stanford, Russ Greiner worked in both academic and industrial research before settling at the University of Alberta, where he is now a Professor in Computing Science and the founding Scientific Director of the Alberta Ingenuity Centre for Machine Learning, which won the ASTech Award for "Outstanding Leadership in Technology" in 2006. He has been Program Chair for the 2004 "Int'l Conf. on Machine Learning", Conference Chair for 2006 "Int'l Conf. on Machine Learning", Editor-in-Chief for "Computational Intelligence", and is serving on the editorial boards of a number of other journals. He was elected a Fellow of the AAAI (Association for the Advancement of Artificial Intelligence) in 2007, and was awarded a McCalla Professorship in 2005-06 and a Killam Professorship in 2007. He has published over 100 refereed papers and patents, most in the areas of machine learning and knowledge representation. The main foci of his current work are (1) bioinformatics and medical informatics; (2) learning effective probabilistic models and (3) formal foundations of learnability.