Active feature elicitation: An unified framework

Frontiers in Artificial Intelligence

Artificial Intelligence

Sriraam Natarajan¹  Gautam Kunapuli¹  Srijita Das²  Predrag Radivojac³  Nandini Ramanan⁴ 

[1] Computer Science Department, University of Texas at Dallas, Dallas, TX, United States;Department of Computing Science, University of Alberta, Edmonton, AB, Canada;Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States;Palo Alto Networks, Santa Clara, CA, United States;

关键词: active learning;    feature elicitation;    classification;    healthcare;    sample-efficiency;   

DOI  :  10.3389/frai.2023.1029943

 received in 2022-08-28, accepted in 2023-03-03,  发布年份 2023

来源: Frontiers

【 摘 要 】

We consider the problem of active feature elicitation in which, given some examples with all the features (say, the full Electronic Health Record), and many examples with some of the features (say, demographics), the goal is to identify the set of examples on which more information (say, lab tests) need to be collected. The observation is that some set of features may be more expensive, personal or cumbersome to collect. We propose a classifier-independent, similarity metric-independent, general active learning approach which identifies examples that are dissimilar to the ones with the full set of data and acquire the complete set of features for these examples. Motivated by four real clinical tasks, our extensive evaluation demonstrates the effectiveness of this approach. To demonstrate the generalization capabilities of the proposed approach, we consider different divergence metrics and classifiers and present consistent results across the domains.

【 授权许可】

Unknown   
Copyright © 2023 Das, Ramanan, Kunapuli, Radivojac and Natarajan.