Selection of Relevant Features and Examples in Machine Learning

In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare di erent methods. We close with some challenges for future work in this area. Also a liated with the Intelligent Systems Laboratory, Daimler-Benz Research and Technology Center, 1510 Page Mill Road, Palo Alto, CA 94304.