Cost Learning Network for Imbalanced Classification

This paper proposes a method to improve the performance of imbalanced classification via reinforcement learning and cost-sensitive learning. Since the cost information is usually unavailable for cost-sensitive learning, we incorporate reinforcement learning to optimize the specified metric by adjust...

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Bibliographic Details
Main Authors: Tu, Chun-Yi, Lin, Hsuan-Tien
Format: Conference Proceeding
Language:English
Subjects:
Benchmark testing
Computational complexity
Decision making
Learning (artificial intelligence)
Measurement
Reinforcement learning
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Summary:This paper proposes a method to improve the performance of imbalanced classification via reinforcement learning and cost-sensitive learning. Since the cost information is usually unavailable for cost-sensitive learning, we incorporate reinforcement learning to optimize the specified metric by adjusting the cost-matrix for the underlying cost-sensitive classifiers. Our experiment results show that, with the learned cost-matrix, the cost-sensitive classifiers can achieve better performance on several benchmark imbalanced data sets.
ISSN:2376-6824
DOI:10.1109/TAAI51410.2020.00017