Online Active Learning Paired Ensemble for Concept Drift and Class Imbalance

Online Active Learning Paired Ensemble for Concept Drift and Class Imbalance

Practical applications often require learning algorithms capable of addressing data streams with concept drift and class imbalance. This paper proposes an online active learning paired ensemble for drifting streams with class imbalance. The paired ensemble consists of a long-term stable classifier a...

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Bibliographic Details
Main Authors: Hang Zhang, Weike Liu, Jicheng Shan, Qingbao Liu
Format: Article
Language:English
Published: IEEE 2018-01-01
Series:IEEE Access
Subjects:
Online active learning
ensemble learning
concept drift
class imbalance
Online Access:https://ieeexplore.ieee.org/document/8543581/

Internet

https://ieeexplore.ieee.org/document/8543581/

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