Active semi-supervised learning for multi-target regression

Abstract

Supervised machine learning algorithms usually require sufficient labeled data to perform well. However, obtaining this information can be challenging due to monetary and time constraints. As a possible solution, recent works have proposed the combination of active and semi-supervised learning techniques. Active semi-supervised learning investigates methods to efficiently construct predictive models by incorporating unlabeled data, which is either labeled by a domain expert or pseudolabeled by a model. Despite already being studied in other problems, to the best of our knowledge, active semi-supervised learning has not been applied in the context of multi-target regression, a predictive task where multiple continuous targets must be predicted. In this work, we investigate active semi-supervised learning for multi-target regression. More specifically, we propose, MASSTER, Multi-target Active Semi-Supervised Training for Regression, a novel ensemble method that identifies the most relevant instance-target pairs based on the variance in their predictions. Experiments using 8 benchmark datasets reveal that our method for active learning provides superior results in most of the cases when compared to the current state-of-the-art active learning method for multi-target regression. Further, as its semi-supervised component, our method incorporates a variation of both self-learning (MASSTER-SL) and co-training (MASSTER-CT). Both variants presented better metrics in earlier epochs when compared to MASSTER-AL version with less labeled data especially in smaller datasets.