Instance-based learning of credible label sets

Abstract

Even though instance-based learning performs well in practice, it might be criticized for its neglect of uncertainty: An estimation is usually given in the form of a predicted label, but without characterizing the confidence of this prediction. In this paper, we propose an instance-based learning method that allows for deriving "credible" estimations, namely set-valued predictions that cover the true label of a query object with high probability. Our method is built upon a formal model of the heuristic inference principle underlying instance-based learning.