Blending Query Strategy of Active Learning for Imbalanced Data

Blending Query Strategy of Active Learning for Imbalanced Data

When the data is imbalanced, often observed in the real-world, important minor class instances that are conducive to accurately predicting the decision boundary are less likely to be queried in the active learning for classification task. Therefore, mitigating the effect of the imbalance in learning...

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Bibliographic Details
Main Authors: Gwangsu Kim, Chang D. Yoo
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Access
Subjects:
Active learning
imbalanced learning
minor class
multi-armed bandit
uncertainty
query-by-committee
Online Access:https://ieeexplore.ieee.org/document/9840369/

Internet

https://ieeexplore.ieee.org/document/9840369/

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