高效在线决策树学习与主动特征获取（英文版）

英文标题：Efficient Online Decision Tree Learning with Active Feature Acquisition中文摘要：提出一种基于主动规划神经网络的在线决策树构建方法，利用自适应子模性来选择获取具有最小代价的特征值，通过后验采样算法降低在线预测误差，对各种现实世界中的数据集进行了广泛的实验，并在概念漂移的情况下展示其有效性和灵活性。英文摘要：Constructing decision trees online is a classical machine learning problem.Existing works often assume that features are readily available for eachincoming data point. However, in many real world applications, both featurevalues and the labels are unknown a priori and can only be obtained at a cost.For ex