Paper Citation Record · LEDGER

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets

As of 17 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2506.14306.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.14306 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:24:13.378372Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact2
verified fuzzy26
unresolved10
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57468765-45ea-4c3f-9858-fae59a3aaa4c · outbound

This paper cites arXiv (2015).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets arXiv (2015)

Reference 1

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Observation e28099b4-2986-4cf4-b2d1-42e6d659f79b · outbound

This paper cites AAAI 32(1) (2018) https://doi.org/10.1609/aaai.v32i1.11296.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets AAAI 32(1) (2018) https://doi.org/10.1609/aaai.v32i1.11296

Reference 2

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Observation ce1520b4-c046-49e9-bfe5-4b758fc69073 · outbound

This paper cites Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Human Perceptions of Fairness in Algorithmic Decision Making: A Case Study of Criminal Risk Prediction

Reference 3

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Observation eb1d19f1-4536-4dc8-8527-0876ec5525ef · outbound

This paper cites IEEE Access 10, 120850–120865 (2022) 28 T able 17: Performance Results: Analysis on the Larger Test Collec- tion of CCF dataset.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Access 10, 120850–120865 (2022) 28 T able 17: Performance Results: Analysis on the Larger Test Collec- tion of CCF dataset

Reference 4

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Observation 3aef56ef-6ab3-4f3b-804a-764b69870d96 · outbound

This paper cites Manage- ment Science 65(7), 2966–2981 (2019) https://doi.org/10.1287/mnsc.2018.3093 https://doi.org/10.1287/mnsc.2018.3093.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Manage- ment Science 65(7), 2966–2981 (2019) https://doi.org/10.1287/mnsc.2018.3093 https://doi.org/10.1287/mnsc.2018.3093

Reference 5

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Observation 6525d02f-8ba1-417a-bbf8-8702aca29c54 · outbound

This paper cites MIT Press, ??? (2023).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets MIT Press, ??? (2023)

Reference 6

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Observation e06baeaa-df70-4aa2-9c6c-698653595050 · outbound

This paper cites ACM Comput.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets ACM Comput

Reference 7

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Observation 1ec3f402-50f7-4dce-9ae4-3ef7911af38b · outbound

This paper cites In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the 29th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp

Reference 9

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Observation 30190282-2d11-4721-9264-429a699b70cb · outbound

This paper cites In: 2009 2nd Inter- national Conference on Computer, Control and Communication, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: 2009 2nd Inter- national Conference on Computer, Control and Communication, pp

Reference 10

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Observation d1186ae1-913f-4a52-ba55-fb50814b16a9 · outbound

This paper cites arXiv (2021).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets arXiv (2021)

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.498318Z

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Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Unresolved cited work

Reference 12

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Observation e721f3bd-371e-46ec-983f-99cabbe547bc · outbound

This paper cites IEEE Access (2024).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Access (2024)

Reference 13

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Observation cb04a573-25d8-4528-9d9e-9d3f000c1c29 · outbound

This paper cites In: 2009 IEEE International Conference on Data Mining Workshops, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: 2009 IEEE International Conference on Data Mining Workshops, pp

Reference 14

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Observation a6707a31-f65b-40d7-a6f3-2c5e61da4eb9 · outbound

This paper cites In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp

Reference 15

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Observation 9504740a-018f-4b46-94f4-9c29a4e33213 · outbound

This paper cites In: 2021 International Conference on Applied Artificial Intelligence (ICAPAI), pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: 2021 International Conference on Applied Artificial Intelligence (ICAPAI), pp

Reference 16

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Observation 1f094e65-b8c1-4ef7-baf2-7d520510af8a · outbound

This paper cites arXiv (2019).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets arXiv (2019)

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.449226Z

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Observation 1f882948-6653-4c2f-8be8-ada096f05f8e · outbound

This paper cites arXiv (2018).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets arXiv (2018)

Reference 18

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Observation 6a2dcb43-9ab0-4f7c-93a2-6838ddafb9c4 · outbound

This paper cites Journal of artificial intelligence research 16, 321–357 (2002).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Journal of artificial intelligence research 16, 321–357 (2002)

Reference 19

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Observation 03b0b775-0c7b-47f0-9456-a4b8b4acf30a · outbound

This paper cites Journal of Biomedical Informatics 107, 103465 (2020).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Journal of Biomedical Informatics 107, 103465 (2020)

Reference 20

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Observation 4fb80b99-f06d-46de-9b91-550214e81b05 · outbound

This paper cites In: International Conference on Intelligent Computing, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: International Conference on Intelligent Computing, pp

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.395937Z

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Observation b6e4e0c4-c6fa-40b7-91b1-b070dd42ba65 · outbound

This paper cites In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI), pp

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.381816Z

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Observation 2a571500-23d2-4067-8ced-bb5f99677e06 · outbound

This paper cites In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.366562Z

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Observation 2f03d277-64a0-4ad0-a1b4-9941c46b53c6 · outbound

This paper cites IEEE Access 9, 109960–109975 (2021).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Access 9, 109960–109975 (2021)

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.352126Z

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Observation 05a6c31c-bf0d-4c99-9ec0-6d2e4c575f94 · outbound

This paper cites IEEE Access 7, 93010–93022 (2019).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Access 7, 93010–93022 (2019)

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.337836Z

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Observation 80cc3f3d-bfbb-42c0-84d5-61dc5ebb18fd · outbound

This paper cites IEEE Transactions on Biometrics, Behavior, and Identity Science 5(2), 244–254 (2022).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Transactions on Biometrics, Behavior, and Identity Science 5(2), 244–254 (2022)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.321692Z

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Observation 39bace24-c9b6-4e87-9fd2-448e8acafb1c · outbound

This paper cites IEEE Access11, 56691–56702 (2023).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets IEEE Access11, 56691–56702 (2023)

Reference 27

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This paper cites Expert Systems with Applications 228, 120323 (2023).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Expert Systems with Applications 228, 120323 (2023)

Reference 28

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Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: 2015 IEEE Symposium Series on Computational Intelligence, pp

Reference 29

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This paper cites Advances in Neural Information Processing Systems (2022).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Advances in Neural Information Processing Systems (2022)

Reference 30

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This paper cites In: Proceedings of the 35th International Conference on Machine Learning, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the 35th International Conference on Machine Learning, pp

Reference 31

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Observation d6021e68-f3ba-44fd-bab6-7149f14fad4a · outbound

This paper cites In: Proceedings of the Conference on Fairness, Accountability, and Transparency.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the Conference on Fairness, Accountability, and Transparency

Reference 32

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This paper cites In: Proceedings of the Conference on Fairness, Accountability, and Transparency.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the Conference on Fairness, Accountability, and Transparency

Reference 33

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This paper cites Fairness-Aware Data Valuation for Supervised Learning.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Fairness-Aware Data Valuation for Supervised Learning

Reference 34

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Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Information sciences 501, 118–135 (2019)

Reference 35

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This paper cites Expert Systems with Applications 178, 115011 (2021).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Expert Systems with Applications 178, 115011 (2021)

Reference 36

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This paper cites BMC genomics 21, 1–13 (2020).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets BMC genomics 21, 1–13 (2020)

Reference 37

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This paper cites Pattern Recognition Letters 136, 71–80 (2020).

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Pattern Recognition Letters 136, 71–80 (2020)

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.151787Z

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This paper cites In: Proceedings of the 30th International Conference on Machine Learning, pp.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Proceedings of the 30th International Conference on Machine Learning, pp

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.135877Z

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This paper cites Neurocomputing 415, 295–316 (2020) https://doi.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets Neurocomputing 415, 295–316 (2020) https://doi

Reference 40

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This paper cites In: Esparza, J., Majumdar, R.

Fair for a few: Improving Fairness in Doubly Imbalanced Datasets In: Esparza, J., Majumdar, R

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:24:14.120902Z

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Fair for a few: Improving Fairness in Doubly Imbalanced Datasets https: 32 //doi.org/10.1145/3287560.3287586

Reference 328

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Pith citing papers

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