Active learning of label ranking functions

The effort necessary to construct labeled sets of examples in a supervised learning scenario is often disregarded, though in many applications, it is a time-consuming and expensive procedure. While this already constitutes a major issue in classification learning, it becomes an even more serious problem when dealing with the more complex target domain of total orders over a set of alternatives. Considering both the pairwise decomposition and the constraint classification technique to represent label ranking functions, we introduce a novel generalization of pool-based active learning to address this problem.