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An efficient and straightforward online vector quantization method for a data stream through remove-birth updating.
1Komatsu University, Komatsu, Ishikawa, Japan.
Peerj. Computer Science
|January 23, 2024
Summary
This study introduces a simple online vector quantization method to handle concept drift in data streams. The technique efficiently adapts to changing data characteristics, minimizing dead units and aiding drift detection.
Area of Science:
- Computer Science
- Data Science
- Machine Learning
Background:
- Exponential growth in network-connected devices generates massive data streams.
- Data streams present analysis challenges due to continuous generation and dynamic characteristic changes (concept drift).
Purpose of the Study:
- To propose a simple online vector quantization method for effectively handling concept drift in data streams.
- To develop a method that efficiently reduces data volume while adapting to dynamic data characteristics.
Main Methods:
- A novel online vector quantization approach utilizing remove-birth updating.
- Identifying and replacing low win probability units to adapt to concept drift.
Main Results:
- The proposed method demonstrates rapid adaptation to concept drift.
- Minimal generation of dead units observed even under concept drift conditions.
- Metrics derived from the method show potential for drift detection.
Conclusions:
- The simple online vector quantization method is effective for concept drift adaptation in data streams.
- The method offers efficient data stream analysis and aids in drift detection.
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