CMAL: Cost-Effective Multi-Label Active Learning by Querying Subexamples

CMAL: Cost-Effective Multi-Label Active Learning by Querying Subexamples

Multi-label active learning (MAL) aims to learn an accurate multi-label classifier by selecting which examples (or example-label pairs) will be annotated and reducing query effort. MAL is a more complicated and expensive process than single-label active learning, due to one example can be associated...

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Bibliographic Details
Main Authors: Chen, X. (Author), Domeniconi, C. (Author), Li, Z. (Author), Wang, J. (Author), Yu, G. (Author), Zhang, X. (Author), Zhang, Z. (Author)
Format: Article
Language:English
Published: IEEE Computer Society 2022
Subjects:
Active Learning
Artificial intelligence
Classification (of information)
Cost effective
Cost effectiveness
Costs
label correlation
Label correlations
Label space
label sparsity
Label sparsity
Learning systems
multi-instance learning
Multi-instance learning
Multi-label active learning
Multi-labels
representative
Representative
uncertainty
Uncertainty
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