A concept-drift perspective on prototype selection and generation

This study brings together systematised views of two related areas: data editing for the nearest neighbour classifier and adaptive learning in the presence of concept drift. The growing number of studies in the intersection of these areas warrants a closer look. We revise and update the taxonomies of the two areas proposed in the literature and argue that they are not sufficiently discriminative with respect to methods for prototype selection and prototype generation in the presence of concept drift. We proceed to create a bespoke taxonomy of these methods and illustrate it with ten examples from the literature. The new taxonomy can serve as a road-map for researching the intersection area and inform the development of new methods.