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 labelingcost 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.

中文翻译：

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回归任务的主动学习合奏方法

主动学习是从带标签的和未带标签的示例中学习的一种典型方法，旨在在专家指导下以最小的费用构建有效且准确的预测模型。由于在许多科学领域中都缺乏标记数据，而同时，未标记数据的标记成本通常在时间和支出方面都很高，因此主动学习在近几年迅速增长，并取得了巨大的成功。这反映在提供洞察力并分析几种主动学习方法的各种研究中，尤其是在分类任务的情况下，而关于回归学习的主动学习方法的实施的研究数量有限。在这种情况下，本文提出了一种基于池的主动学习回归算法，该算法采用委员会查询策略来评估未标记实例的信息量。在大量基准数据集上的实验结果证明了该方法的有效性，因为它优于采用随机抽样策略以及熟悉的监督方法的基线主动学习方法。