Conformal prediction based active learning by linear regression optimization

Conformal prediction uses the degree of strangeness (nonconformity) of data instances to determine the confidence values of new predictions. We propose a conformal prediction based active learning algorithm, referred to as CPAL-LR, to improve the performance of pattern classification algorithms. CPAL-LR uses a novel query function that determines the relevance of unlabeled instances through the solution of a constrained linear regression model, incorporating uncertainty, diversity, and representativeness in the optimization problem. Furthermore, we present a nonconformity measure that produces reliable confidence values. CPAL-LR is implemented in conjunction with support vector machines, sparse coding algorithms, and convolutional networks. Experiments conducted on face and object recognition databases demonstrate that CPAL-LR improves the classification performance of a variety classifiers, outperforming previously proposed active learning techniques, while producing reliable confidence values. (C) 2020 Elsevier B.V. All rights reserved.