The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Cost-effective Batch-mode Multi-label Active Learning
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In the citing paper
“Clustering algorithms, for example, have been widely applied to optimise criteria such as diversity [49], representativeness [67], rarity [15,50], and density [16,78].”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [49]
Citations
Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
A Review on Multi-Label Learning Algorithms
Zhang, M.-L., & Zhou, Z.-H. (2014) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2013.39 · Cited by 3,321
A survey on semi-supervised learning
van Engelen, J. E., & Hoos, H. H. (2020) · Machine Learning · doi:10.1007/s10994-019-05855-6 · Cited by 2,666
Learning multi-label scene classification
Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004) · Pattern Recognition · doi:10.1016/j.patcog.2004.03.009 · Cited by 2,296
Improved Heterogeneous Distance Functions
Wilson, D. R., & Martinez, T. R. (1997) · Journal of Artificial Intelligence Research · doi:10.1613/jair.346 · Cited by 1,344
An analysis of active learning strategies for sequence labeling tasks
Settles, B., & Craven, M. (2008) · Proceedings of the Conference on Empirical Methods in Natural Language Processing - EMNLP '08 · doi:10.3115/1613715.1613855 · Cited by 972
The challenge problem for automated detection of 101 semantic concepts in multimedia
Snoek, C. G. M., Worring, M., van Gemert, J. C., Geusebroek, J.-M., & Smeulders, A. W. M. (2006) · Proceedings of the 14th ACM international conference on Multimedia · doi:10.1145/1180639.1180727 · Cited by 620
Research works printed but not linked: 1
- Zhang, 2014, Multilabel image classification via high-order label correlation driven active learning, IEEE Transactions on Image Processing, 23, 1430, 10.1109/TIP.2014.2302675 · DOI
Cited by
Ten Years of Active Learning Techniques and Object Detection: A Systematic Review
Garcia, D., Carias, J., Adão, T., Jesus, R., Cunha, A., & Magalhães, L. G. (2023) · Applied Sciences · doi:10.3390/app131910667 · Cited by 17
Metadata
- Authors
- Gui, X., Lu, X., & Yu, G.
- Affiliations
- Shandong University (CN)
- Venue
- Neurocomputing
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 463
- Pages
- 355-367
- ISSN
- 0925-2312 (ISSN-L)
- OpenAlex
- W3193305990
- Cited by
- 22
- References
- 54
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 1 · 2023: 12 · 2022: 4 · 2021: 1
- FWCI
- 1.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Neurocomputing (publishedVersion)
- Funders
- Shandong University
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- batch mode active learning, multi-label classification, query cost reduction, uncertainty, label correlation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cost-effective Batch-mode Multi-label Active Learning. https://app.cochat.ai/papers/10.1016/j.neucom.2021.08.063
- The paper
- Gui, X., Lu, X., & Yu, G. (2021). Cost-effective Batch-mode Multi-label Active Learning. Neurocomputing. https://doi.org/10.1016/j.neucom.2021.08.063
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
