Analysis of Mixed Concept Drift Detectors in Deployed Machine Learning Models

Analysis of Mixed Concept Drift Detectors in Deployed Machine Learning Models

Zamfirescu, Toma (TU Delft Electrical Engineering, Mathematics and Computer Science)

Poenaru-Olaru, L. (mentor)
Rellermeyer, Jan S. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-02-03

Label-independent concept drift detectors represent an emerging topic in machine learning research, especially in models deployed in a production environment where obtaining labels can become increasingly difficult and costly. Concept drift refers to unforeseeable changes in the distribution of data streams, which directly impact the performance of a model trained on historical data. This paper initially focuses on two mixed label-independent drift detectors, SQSI and UDetect, which are implemented and evaluated on a specific setup using synthetic and real-world data sets. Next, multiple label-dependent drift detectors are evaluated on real-world data sets, and the results are compared to those of the label-independent detectors. This paper presents a framework for comparing multiple concept drift detectors on different data sets and configurations, checking whether they can be reliably used in a production environment.

http://resolver.tudelft.nl/uuid:22d337d6-e56e-4f13-9842-af7a82615922

Student theses

bachelor thesis

© 2023 Toma Zamfirescu