论文标题
基于图的积极学习方法在回归中
A Graph-Based Approach for Active Learning in Regression
论文作者
论文摘要
主动学习旨在通过有选择地要求人类从未标记的池中注释最重要的数据点来减少标签工作，并且是人机相互作用的一个例子。尽管积极学习已被广泛研究用于分类和排名问题，但对于回归问题，它相对研究了。回归方法的大多数现有主动学习都使用在每个主动学习迭代中学习的回归函数，以选择下一个信息查询点。这引入了一些挑战，例如处理嘈杂标签，参数不确定性和克服最初偏见的培训数据。取而代之的是，我们提出了一种以功能为中心的方法，该方法将顺序模式和批处理模式活跃回归作为一种新颖的两分图优化问题。我们对无噪声和嘈杂的设置进行实验。我们对基准数据集的实验结果证明了我们提出的方法的有效性。
Active learning aims to reduce labeling efforts by selectively asking humans to annotate the most important data points from an unlabeled pool and is an example of human-machine interaction. Though active learning has been extensively researched for classification and ranking problems, it is relatively understudied for regression problems. Most existing active learning for regression methods use the regression function learned at each active learning iteration to select the next informative point to query. This introduces several challenges such as handling noisy labels, parameter uncertainty and overcoming initially biased training data. Instead, we propose a feature-focused approach that formulates both sequential and batch-mode active regression as a novel bipartite graph optimization problem. We conduct experiments on both noise-free and noisy settings. Our experimental results on benchmark data sets demonstrate the effectiveness of our proposed approach.
