Towards a classifier for predicting cardiovascular events using privileged information

Abstract

Learning Using Privileged Information (LUPI) is a learning paradigm that aims to improve supervised learning in the presence of additional (privileged) information available during training, but not during the testing phase. For example, the Multi-Ethnic Study of Atherosclerosis (MESA) used in epidemiological studies related to heart disease, contains data from 186 attributes, only eight of which are used in current risk prediction algorithms.