论文标题
图中节点响应预测的元学习学习
Meta-Active Learning for Node Response Prediction in Graphs
论文作者
论文摘要
元学习是提高机器学习绩效的重要方法，目标任务的观察数量有限。但是，当观察结果不平衡时，即使使用元学习方法，也很难提高性能。在本文中，我们提出了一种主动学习方法，用于在属性图中对节点响应预测任务进行元学习，其中选择了要观察的节点以提高性能，以尽可能少的观察到的节点。通过提出的方法，我们使用基于图形卷积神经网络的模型来预测节点响应和选择节点，我们可以通过这些模型预测响应并选择响应变量的图形，甚至可以选择节点。响应预测模型是通过最小化预期测试误差来训练的。节点选择模型通过通过增强学习最大化预期的误差来训练。我们通过11种类型的道路拥堵预测任务证明了该方法的有效性。
Meta-learning is an important approach to improve machine learning performance with a limited number of observations for target tasks. However, when observations are unbalancedly obtained, it is difficult to improve the performance even with meta-learning methods. In this paper, we propose an active learning method for meta-learning on node response prediction tasks in attributed graphs, where nodes to observe are selected to improve performance with as few observed nodes as possible. With the proposed method, we use models based on graph convolutional neural networks for both predicting node responses and selecting nodes, by which we can predict responses and select nodes even for graphs with unseen response variables. The response prediction model is trained by minimizing the expected test error. The node selection model is trained by maximizing the expected error reduction with reinforcement learning. We demonstrate the effectiveness of the proposed method with 11 types of road congestion prediction tasks.
