Concept Drift Monitoring and Diagnostics of Supervised Learning Models via Score Vectors
Author:
Affiliation:
1. Department of Industrial Engineering and Management Sciences, Northwestern University, Evanston, IL
2. Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, Richmond, VA
Funder
Air Force Office of Scientific Research
Extreme Science and Engineering Discovery Environment
Publisher
Informa UK Limited
Subject
Applied Mathematics,Modeling and Simulation,Statistics and Probability
Link
https://www.tandfonline.com/doi/pdf/10.1080/00401706.2022.2124310
Reference44 articles.
1. A large-scale comparison of concept drift detectors
2. Learning from Time-Changing Data with Adaptive Windowing
3. Optimization Methods for Large-Scale Machine Learning
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