On metafeatures’ ability of implicit concept identification

Concept drift in data stream processing remains an intriguing challenge and states a popular research topic. Methods that actively process data streams usually employ drift detectors, whose performance is often based on monitoring the variability of different stream properties. This publication prov...

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Bibliographic Details
Published in:Machine learning 2024-10, Vol.113 (10), p.7931-7966
Main Authors: Komorniczak, Joanna, Ksieniewicz, Paweł
Format: Article
Language:English
Subjects:
Algorithms
Artificial Intelligence
Batch processing
Classification
Computer Science
Control
Data transmission
Drift
Machine Learning
Mechatronics
Methods
Natural Language Processing (NLP)
Robotics
Sensors
Simulation and Modeling
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Summary:Concept drift in data stream processing remains an intriguing challenge and states a popular research topic. Methods that actively process data streams usually employ drift detectors, whose performance is often based on monitoring the variability of different stream properties. This publication provides an overview and analysis of metafeatures variability describing data streams with concept drifts. Five experiments conducted on synthetic, semi-synthetic, and real-world data streams examine the ability of over 160 metafeatures from 9 categories to recognize concepts in non-stationary data streams. The work reveals the distinctions in the considered sources of streams and specifies 17 metafeatures with a high ability of concept identification.
ISSN:0885-6125
1573-0565
DOI:10.1007/s10994-024-06612-0