{"type":"ScholarlyArticle","title":"Efficient Online Decision Tree Learning with Active Feature Acquisition","authors":[{"name":"Arman Rahbar","slug":"arman-rahbar","given":"Arman","family":"Rahbar","affiliation":"Chalmers University of Technology"},{"name":"Ziyu Ye","slug":"ziyu-ye","given":"Ziyu","family":"Ye","affiliation":"University of Chicago"},{"name":"Yuxin Chen","slug":"yuxin-chen","given":"Yuxin","family":"Chen","affiliation":"University of Chicago"},{"name":"Morteza Haghir Chehreghani","slug":"morteza-haghir-chehreghani","given":"Morteza","family":"Haghir Chehreghani","affiliation":"Chalmers University of Technology"}],"abstract":"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 shown to be competitive with baseline models while being more flexible.","doi":"10.24963/ijcai.2023/463","url":"https://ijcai.org/proceedings/2023/463","pdf":"https://ijcai.org/proceedings/2023/0463.pdf","markdown":"https://ijcai.org/proceedings/2023/463.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/463","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":463,"pages":{"first":4163,"last":4171},"keywords":["Machine Learning: ML: Active learning","Machine Learning: ML: Online learning","Data Mining: DM: Mining data streams"],"videos":[],"references":[{"position":1,"raw":"[Agrawal and Jia, 2017] Shipra Agrawal and Randy Jia. 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