The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Active Learning by Querying Informative and Representative Examples
Abstract
Active learning reduces the labeling cost by iteratively selecting the most valuable data to query their labels. It has attracted a lot of interests given the abundance of unlabeled data and the high cost of labeling. Most active learning approaches select either informative or representative unlabeled instances to query their labels, which could significantly limit their performance. Although several active learning algorithms were proposed to combine the two query selection criteria, they are usually ad hoc in finding unlabeled instances that are both informative and representative. We address this limitation by developing a principled approach, termed QUIRE, based on the min-max view of active learning. The proposed approach provides a systematic way for measuring and combining the informativeness and representativeness of an unlabeled instance. Further, by incorporating the correlation among labels, we extend the QUIRE approach to multi-label learning by actively querying instance-label pairs. Extensive experimental results show that the proposed QUIRE approach outperforms several state-of-the-art active learning approaches in both single-label and multi-label learning.
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Cites
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
A Sequential Algorithm for Training Text Classifiers
Lewis, D. D., & Gale, W. A. (1994) · SIGIR ’94 · doi:10.1007/978-1-4471-2099-5_1 · Cited by 1,208
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A brief introduction to weakly supervised learning
Zhou, Z.-H. (2018) · National Science Review · doi:10.1093/nsr/nwx106 · Cited by 1,899
Self-citationExplainable Active Learning (XAL): Toward AI Explanations as Interfaces for Machine Teachers
Ghai, B., Liao, Q. V., Zhang, Y., Bellamy, R., & Mueller, K. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3432934 · Cited by 106
Fast hierarchical clustering of local density peaks via an association degree transfer method
Guan, J., Li, S., He, X., Zhu, J., & Chen, J. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.05.071 · Cited by 73
Wu, D., Lawhern, V. J., Hairston, W. D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2016.2544108 · Cited by 74
Marathe, A. R., Lawhern, V. J., Wu, D., Slayback, D., & Lance, B. J. (2016) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2015.2502323 · Cited by 64
Temporal Coherence for Active Learning in Videos
Zolfaghari Bengar, J., Gonzalez-Garcia, A., Villalonga, G., Raducanu, B., Habibi Aghdam, H., Mozerov, M., Lopez, A. M., & van de Weijer, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00120 · Cited by 41
Cost-effective Batch-mode Multi-label Active Learning
Gui, X., Lu, X., & Yu, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.063 · Cited by 22
QBox: Partial Transfer Learning With Active Querying for Object Detection
Tang, Y.-P., Wei, X.-S., Zhao, B., & Huang, S.-J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3111621 · Cited by 17
Self-citationHuo, Y., Zhao, F., Zhang, H., Zhuang, S., & Sun, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/1556768 · Cited by 8
Survey of Computerized Adaptive Testing: A Machine Learning Perspective
Zhuang, Y., Liu, Q., Bi, H., Huang, Z., Huang, W., Li, J., Yu, J., Liu, Z., Hu, Z., Hong, Y., et al. (2026) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2026.3672850 · Cited by 9
“This technique has improved data efficiency in numerous learning tasks [130].”
Metadata
- Authors
- Huang, S.-J., Jin, R., & Zhou, Z.-H.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2014-10
- Volume
- 36
- Issue
- 10
- Pages
- 1936-1949
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2012878613
- Cited by
- 440
- References
- 2
- FWCI
- 29.05
- Type
- journal article
- Language
- en
- Open access
- closed
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