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While active learning for classification problems has received considerable attention in recent years, studies on problems of regression are rare. This paper provides a systematic review of the most commonly used selection strategies for active learning within the context of linear regression. The recently developed Exploration Guided Active Learning (EGAL) algorithm, previously deployed within a classification context, is explored as a selection strategy for regression problems. Active learning is demonstrated to significantly improve the learning rate of linear regression models. Experimental results show that a purely diversity-based approach to
O'Neill J. (2015) An Evaluation of Selection Strategies for Active Learning with Regression. A dissertation submitted in partial fulfilment of the requirements of Dublin Institute of Technology for the degree of M.Sc. in Computing (Data Analytics), September 2015.