Evaluating Data Distribution Based Concept Drift Detectors

Evaluating Data Distribution Based Concept Drift Detectors

Kanniainen, Konsta (TU Delft Electrical Engineering, Mathematics and Computer Science)

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

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-02-03

Various techniques have been studied to handle unexpected changes in data streams, a phenomenon called concept drift. When the incoming data is not labeled and the labels are also not obtainable with a reasonable effort, detecting these drifts becomes less trivial. This study evaluates how well two data distribution based label-independent drift detection methods, SyncStream and Statistical Change Detection for Multi-Dimensional Data, detect concept drift. This is done by implementing the algorithms and evaluating them side by side on both synthetic and real-world datasets. The metrics used for synthetic datasets are False Positive Rate and Latency; for real-world datasets, Accuracy is used instead of Latency. The experiments show that both drift detectors perform significantly worse on real-world than on synthetic data.

concept drift
machine learning
Data Stream Synchronization

http://resolver.tudelft.nl/uuid:86e9c0ff-13eb-4e4d-8eb1-5045aacf666a

Student theses

bachelor thesis

© 2023 Konsta Kanniainen