Active Learning for Regression and Classification by Inverse Distance Weighting

This paper proposes an active learning algorithm for solving regression and classification 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 independent of the type of predictor used; (iii) can handle known and unknown constraints on the queryable feature vectors; and (iv) can run either sequentially, or in batch mode, depending on how often the predictor is retrained. The method's potential is shown in numerical tests on illustrative synthetic problems and real-world regression and classification datasets from the UCI repository. A Python implementation of the algorithm that we call IDEAL (Inverse-Distance based Exploration for Active Learning), is available at \url{http://cse.lab.imtlucca.it/~bemporad/ideal}.