Learning classification models with soft-label information.
Learning of classification models in medicine often relies on data labeled by a human expert. Since labeling of clinical data may be time-consuming, finding ways of alleviating the labeling costs is critical for our ability to automatically learn such models. In this paper we propose a new machine learning approach that is able to learn improved binary classification models more efficiently by refining the binary class information in the training [...]
Author(s): Nguyen, Quang, Valizadegan, Hamed, Hauskrecht, Milos
DOI: 10.1136/amiajnl-2013-001964