An active learning ensemble method for regression tasks

• Published in: Intelligent data analysis Vol. 24; no. 3; pp. 607 - 623
• Main Authors: Fazakis, Nikos, Kostopoulos, Georgios, Karlos, Stamatis, Kotsiantis, Sotiris, Sgarbas, Kyriakos
• Format: Journal Article
• Language: English
• Published: Amsterdam IOS Press BV 01.01.2020
• Subjects: Active learning; Algorithms; Machine learning; Prediction models; Random sampling; Regression analysis; Teaching methods
• DOI: 10.3233/IDA-194608
• ISSN: 1088-467X

Active learning is a typical approach for learning from both labeled and unlabeled examples aiming to build efficient and accurate predictive models at minimum expense under an expert’s guidance. Since there is a lack of labeled data in many scientific fields whilst, at the same time, the labeling cost of unlabeled data is typically high in terms of time and expenditure, active learning has grown rapidly over recent years with great success. This is reflected in various studies providing insights and analyzing several active learning methods, especially in the case of classification tasks, whereas, there is only a limited number of studies concerning the implementation of active learning methods for regression ones. Within this context, the present paper sets out to put forward a pool-based active learning regression algorithm employing the query by committee strategy to evaluate the informativeness of unlabeled examples. The experimental results on a plethora of benchmark datasets demonstrate the efficiency of the proposed method, since it prevails over the baseline active learning approach applying the random sampling strategy, as well as familiar supervised methods.