In this survey, w e review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two k ey 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
The work in REF discusses various developments in machine learning to the problem of selecting relevant features, and the problem of selecting relevant examples which is otherwise the feature selection problem currently dealt in this research.
Selection of relevant features and examples in machine learning
Artificial Intelligence
Computer Science