Active learning for regression by inverse distance weighting

This paper proposes an active learning (AL) algorithm to solve regression problems based on inverse-distance weighting functions for selecting the feature vectors to query. The algorithm has the following features: (i) supports both pool-based and population-based sampling; (ii) is not tailored to a particular class of predictors; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequen-tially, or in batch mode, depending on how often the predictor is retrained. The potentials of the method are shown in numerical tests on illustrative synthetic problems and real -world datasets. An implementation of the algorithm, that we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at http://cse.lab. imtlucca.it/bemporad/ideal.(c) 2023 Elsevier Inc. All rights reserved.