Online Active Learning for Drifting Data Streams
| Autor: | Jie Cao, Jian Yang, Chuan Zhou, Shan Xue, Sanmin Liu, Zhao Li, Jia Wu |
|---|---|
| Rok vydání: | 2023 |
| Předmět: |
Forgetting curve
Concept drift Computer Networks and Communications Computer science Data stream mining Generalization Active learning (machine learning) business.industry Machine learning computer.software_genre Field (computer science) Computer Science Applications Data modeling Artificial Intelligence Artificial Intelligence & Image Processing Noise (video) Artificial intelligence business computer Software |
| Zdroj: | IEEE Transactions on Neural Networks and Learning Systems. 34:186-200 |
| ISSN: | 2162-2388 2162-237X |
| Popis: | Classification methods for streaming data are not new, but very few current frameworks address all three of the most common problems with these tasks: concept drift, noise, and the exorbitant costs associated with labeling the unlabeled instances in data streams. Motivated by this gap in the field, we developed an active learning framework based on a dual-query strategy and Ebbinghaus's law of human memory cognition. Called CogDQS, the query strategy samples only the most representative instances for manual annotation based on local density and uncertainty, thus significantly reducing the cost of labeling. The policy for discerning drift from noise and replacing outdated instances with new concepts is based on the three criteria of the Ebbinghaus forgetting curve: recall, the fading period, and the memory strength. Simulations comparing CogDQS with baselines on six different data streams containing gradual drift or abrupt drift with and without noise show that our approach produces accurate, stable models with good generalization ability at minimal labeling, storage, and computation costs. |
| Databáze: | OpenAIRE |
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