Learning a logistic regression with the help of unknown features at prediction stage

Abstract

The use of features available at training time, but not at prediction time, as additional information for training models is known as learning using privileged information paradigm. In this paper, the handling of privileged features is addressed from the logistic regression perspective, commonly used in the clinical setting. Two new proposals, LOGIT+ and LRPROB+, learned with the influence of privileged features and preserving the interpretability of conventional logistic regression, are proposed. Experimental results on datasets report improvements of our proposals over the performance of traditional logistic regression learned without privileged information.