论文标题
在基于池的顺序积极学习中整合信息性，代表性和多样性以进行回归
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression
论文作者
论文摘要
在许多现实世界的机器学习应用程序中，未标记的样品很容易获得，但是标签它们是昂贵和/或耗时的。主动学习是减少此数据标签工作的常见方法。它最佳选择了标签的最佳样本，因此可以从相同数量的标记样本中训练更好的机器学习模型。本文考虑了积极的回归学习（ALR）问题。已经为ALR提出了三个基本标准 - 信息性，代表性和多样性。但是，文献中很少有三种方法同时考虑了这三个方法。我们提出了三种新的ALR方法，并采用了整合三个标准的不同策略。在各个领域的12个数据集上进行了广泛的实验证明了它们的有效性。
In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.
