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Efficient Online Decision Tree Learning with Active Feature Acquisition by Rahbar, Arman; Ye, Ziyu; Chen, Yuxin; Chehreghani, Morteza Haghir is a scholarly article available to read on EtoBox.

What is Efficient Online Decision Tree Learning with Active Feature Acquisition about?

Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, in many real world applications, both feature values and the labels are unknown a priori and can only be obtained at a cost. For example, in medical diagnosis, doctors have to choose which tests to perform (i.e., making costly feature queries) on a patient in order to make a diagnosis decision (i.e., predicting labels). We provide a fresh perspective to tackle this practical challenge. Our framework consists of an active planning oracle embedded in an online learning scheme for which we investigate several information acquisition functions. Specifically, we employ a surrogate information acquisition function based on adaptive submodularity to actively query feature values with a minimal cost, while using a posterior sampling scheme to maintain a low regret for online prediction. We demonstrate the efficiency and effectiveness of our framework via extensive experiments on various real-world datasets. Our framework also naturally adapts to the challenging setting of online learning with concept drift and is

Author

Rahbar, Arman; Ye, Ziyu; Chen, Yuxin; Chehreghani, Morteza Haghir

Published

2023

Language

EN