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Paper Citation Record · LEDGER

Resampling strategies for imbalanced regression: a survey and empirical analysis

As of 9 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 0 inbound Pith citation observations for arXiv:2507.11902.

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

pith.paper-citation-record.v1
2507.11902 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:04:14.480768Z

measured 55 of 55 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

55 of 55 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a0e6d28-aa6a-4784-9329-aae1c4713d23 · outbound

This paper cites Expert systems with applications 73, 220–239 (2017).

Resampling strategies for imbalanced regression: a survey and empirical analysis Expert systems with applications 73, 220–239 (2017)

Reference 1

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Observation b4949020-8dbf-4712-bc1d-cdb8603a1771 · outbound

This paper cites Progress in Artificial Intelligence 5(4), 221–232 (2016).

Resampling strategies for imbalanced regression: a survey and empirical analysis Progress in Artificial Intelligence 5(4), 221–232 (2016)

Reference 2

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Observation 56c23084-3e83-4029-a064-0231cb706b40 · outbound

This paper cites Journal of Big Data 6(1), 1–54 (2019).

Resampling strategies for imbalanced regression: a survey and empirical analysis Journal of Big Data 6(1), 1–54 (2019)

Reference 3

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This paper cites UBL: an R package for Utility-based Learning.

Resampling strategies for imbalanced regression: a survey and empirical analysis UBL: an R package for Utility-based Learning

Reference 4

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Observation ade28d71-eef8-4969-a774-d70745aba36e · outbound

This paper cites In: First International Workshop on Learning with Imbalanced Domains: Theory and Applications, vol.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: First International Workshop on Learning with Imbalanced Domains: Theory and Applications, vol

Reference 5

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Resampling strategies for imbalanced regression: a survey and empirical analysis Unresolved cited work

Reference 6

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Observation c10eb0d0-feda-492e-a205-8213267f97bf · outbound

This paper cites Knowledge-Based Systems 119, 232–256 (2017).

Resampling strategies for imbalanced regression: a survey and empirical analysis Knowledge-Based Systems 119, 232–256 (2017)

Reference 7

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Observation 28827ec1-2d39-40d4-9632-73350b672afd · outbound

This paper cites Machine Learning 109(9), 1803–1835 (2020).

Resampling strategies for imbalanced regression: a survey and empirical analysis Machine Learning 109(9), 1803–1835 (2020)

Reference 8

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Observation 973c93ec-2822-4c24-bd43-48cf5f380883 · outbound

This paper cites In: Portuguese Conference on Artificial Intelligence, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: Portuguese Conference on Artificial Intelligence, pp

Reference 9

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Observation 1136b1fc-c719-45a3-b31d-00307f1898cb · outbound

This paper cites Neurocomputing 343, 76–99 (2019).

Resampling strategies for imbalanced regression: a survey and empirical analysis Neurocomputing 343, 76–99 (2019)

Reference 10

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Observation 1f8e38df-14e0-4d6e-b04e-4b825327448d · outbound

This paper cites In: ICSOFT, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: ICSOFT, pp

Reference 11

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Observation e399c349-5bc6-415a-83df-188cef0a3f80 · outbound

This paper cites IEEE Transactions on Reliability 69(4), 1355–1375 (2020).

Resampling strategies for imbalanced regression: a survey and empirical analysis IEEE Transactions on Reliability 69(4), 1355–1375 (2020)

Reference 12

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Observation 3ef80e3d-025a-4031-ac19-abb6b63a7bf8 · outbound

This paper cites Expert Systems with Applications 82, 357–382 (2017).

Resampling strategies for imbalanced regression: a survey and empirical analysis Expert Systems with Applications 82, 357–382 (2017)

Reference 13

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Observation dc2b842c-c2ef-433b-abe4-f348a0e075b6 · outbound

This paper cites Journal of Chemical Information and Modeling 60(8), 4098–4107 (2020).

Resampling strategies for imbalanced regression: a survey and empirical analysis Journal of Chemical Information and Modeling 60(8), 4098–4107 (2020)

Reference 14

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Observation ce1524b2-108d-403d-af76-f3f9ccf27dd3 · outbound

This paper cites Toxics 9(12), 333 (2021).

Resampling strategies for imbalanced regression: a survey and empirical analysis Toxics 9(12), 333 (2021)

Reference 15

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Observation d8e71df7-a428-4330-84b6-2a917345080d · outbound

This paper cites In: International Conference on Discovery Science, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: International Conference on Discovery Science, pp

Reference 16

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Observation c693f32c-18c5-41b2-bb0d-e8c2b842f38a · outbound

This paper cites Expert Systems with Applications 158, 113026 (2020).

Resampling strategies for imbalanced regression: a survey and empirical analysis Expert Systems with Applications 158, 113026 (2020)

Reference 17

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Observation 636ba9db-c8b2-4900-9adb-27a1952745cc · outbound

This paper cites Applied Soft Computing 83, 105662 (2019).

Resampling strategies for imbalanced regression: a survey and empirical analysis Applied Soft Computing 83, 105662 (2019)

Reference 18

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Observation fd47c0ab-900f-494b-842d-27a13b0ac577 · outbound

This paper cites Foundations of Computing and Decision Sciences 42(2), 149–176 (2017).

Resampling strategies for imbalanced regression: a survey and empirical analysis Foundations of Computing and Decision Sciences 42(2), 149–176 (2017)

Reference 19

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This paper cites Neurocomput- ing 286, 179–192 (2018) 42.

Resampling strategies for imbalanced regression: a survey and empirical analysis Neurocomput- ing 286, 179–192 (2018) 42

Reference 20

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Observation 4ebc2006-41c9-4fe1-8213-5837a542cf41 · outbound

This paper cites International Journal of Engineering & Technology 8, 390–397 (2019).

Resampling strategies for imbalanced regression: a survey and empirical analysis International Journal of Engineering & Technology 8, 390–397 (2019)

Reference 21

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This paper cites In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: 2015 IEEE Trustcom/BigDataSE/ISPA, vol

Reference 22

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This paper cites Information Sciences 325, 98–117 (2015).

Resampling strategies for imbalanced regression: a survey and empirical analysis Information Sciences 325, 98–117 (2015)

Reference 23

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This paper cites Knowledge-Based Systems 227, 107222 (2021).

Resampling strategies for imbalanced regression: a survey and empirical analysis Knowledge-Based Systems 227, 107222 (2021)

Reference 24

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Observation 1bce8de9-0ba1-47bf-bb5e-fcadd1a28b02 · outbound

This paper cites International Journal of Pattern Recognition and Artificial Intelligence 33(11), 1940009 (2019).

Resampling strategies for imbalanced regression: a survey and empirical analysis International Journal of Pattern Recognition and Artificial Intelligence 33(11), 1940009 (2019)

Reference 25

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This paper cites Pattern Recognition 57, 164–178 (2016).

Resampling strategies for imbalanced regression: a survey and empirical analysis Pattern Recognition 57, 164–178 (2016)

Reference 26

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This paper cites A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework.

Resampling strategies for imbalanced regression: a survey and empirical analysis A survey on learning from imbalanced data streams: taxonomy, challenges, empirical study, and reproducible experimental framework

Reference 27

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This paper cites In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp

Reference 28

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This paper cites ACM Computing Surveys (CSUR) 49(2), 1–50 (2016).

Resampling strategies for imbalanced regression: a survey and empirical analysis ACM Computing Surveys (CSUR) 49(2), 1–50 (2016)

Reference 29

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This paper cites Mathematics of Computation 52(186), 471–494 (1989).

Resampling strategies for imbalanced regression: a survey and empirical analysis Mathematics of Computation 52(186), 471–494 (1989)

Reference 30

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Resampling strategies for imbalanced regression: a survey and empirical analysis Unresolved cited work

Reference 31

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This paper cites Computational statistics & data analysis 52(12), 5186–5201 (2008).

Resampling strategies for imbalanced regression: a survey and empirical analysis Computational statistics & data analysis 52(12), 5186–5201 (2008)

Reference 32

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Source-reported events for the cited work

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Observation 99da33db-cfbd-4659-963f-fa055cee1c14 · outbound

This paper cites SIAM 43 Journal on Numerical Analysis 17(2), 238–246 (1980).

Resampling strategies for imbalanced regression: a survey and empirical analysis SIAM 43 Journal on Numerical Analysis 17(2), 238–246 (1980)

Reference 33

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Source-reported events for the cited work

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This paper cites IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(4), 463–484 (2011).

Resampling strategies for imbalanced regression: a survey and empirical analysis IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews) 42(4), 463–484 (2011)

Reference 34

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Source-reported events for the cited work

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Observation 104c7851-9d77-490c-bb4f-080e29ad0455 · outbound

This paper cites Information sciences 250, 113–141 (2013).

Resampling strategies for imbalanced regression: a survey and empirical analysis Information sciences 250, 113–141 (2013)

Reference 35

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:19.064231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.152359Z digest=sha256:d4464395d80a3bc248c96ead82ae4deeeb22618d922879414a65f3d7f80ced49

Observation 76238cdc-e1e1-415e-bb69-9582c97acab1 · outbound

This paper cites In: Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: Second International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:18.934891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.242336Z digest=sha256:f3417947e11709ae39de4a123b07c4a686bd52f1081fab2b76023bf04ab4991b

Observation 8e426630-edda-4d09-9e0c-7abd540f1409 · outbound

This paper cites In: 2018 IEEE 5th International Con- ference on Data Science and Advanced Analytics (DSAA), pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: 2018 IEEE 5th International Con- ference on Data Science and Advanced Analytics (DSAA), pp

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:18.806320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.323529Z digest=sha256:c8f3f0e17c4a06b355b9004f68436242102c5d0cd6e94457ad77935c0ac4bf54

Observation 148cffc9-4037-4ce8-8e22-4770e04e48e7 · outbound

This paper cites In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, vol.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: Proceedings of the First International Workshop on Learning with Imbalanced Domains: Theory and Applications, vol

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:18.648879Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.382093Z digest=sha256:51d4a93e38aeb7452d1992d52afdb09f609e72188968022f9a7951f4b4f84953

Observation 8735d60f-072b-423d-998e-56f602e85a6d · outbound

This paper cites In: European Conference on Principles of Data Mining and Knowledge Discovery, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: European Conference on Principles of Data Mining and Knowledge Discovery, pp

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:18.467126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.441335Z digest=sha256:3c72634f55614c206948c34f8ffc6a4fd78bcd99b21ea8d9def73b1a9316fcb0

Observation a0a7bac5-b56a-43ff-bcfb-0860fd818efc · outbound

This paper cites In: International Conference on Machine Learning, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: International Conference on Machine Learning, pp

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:18.235721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.526729Z digest=sha256:b0adf2fb615a3a2cc65aceb170c67da718a7bf6a08cfa102b90f98897b85f9fc

Observation 8923527a-50a1-44c3-b7b0-8dce000bd7e5 · outbound

This paper cites Machine Learning 110, 2187–2211 (2021).

Resampling strategies for imbalanced regression: a survey and empirical analysis Machine Learning 110, 2187–2211 (2021)

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:17.974111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.612416Z digest=sha256:e8273543e08abc21747820d352a5bb6dc70fce3a8774c3bf4c8786faa8c9f0db

Observation 1e224303-d034-4153-a6f5-85df6b632103 · outbound

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

Resampling strategies for imbalanced regression: a survey and empirical analysis Journal of artificial intelligence research 16, 321–357 (2002)

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:17.675896Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.700247Z digest=sha256:0a085debef09aa6d50c4aa2ab77c947b89657ad52b6f3ba25a8791430a994d1a

Observation 2f288b79-0798-492d-bb9b-df44d17f49cd · outbound

This paper cites Expert Systems with Applications, 116387 (2022).

Resampling strategies for imbalanced regression: a survey and empirical analysis Expert Systems with Applications, 116387 (2022)

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:17.268043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.772904Z digest=sha256:342641514a0daec3c20ebe7d2f1315466cc665a1f57bdfab1cc0dd0f1ad28eea

Observation 1f104692-aeb6-4215-917f-5d80ccb74c29 · outbound

This paper cites : Addressing the curse of imbalanced training sets: one-sided selection.

Resampling strategies for imbalanced regression: a survey and empirical analysis : Addressing the curse of imbalanced training sets: one-sided selection

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:17.007721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.803094Z digest=sha256:762c1b189eea03219ea9b604bfde59ac6895b5b7c3aa2f41abc2193d223fe84c

Observation 162564a6-2619-4d09-85fc-1c05da8e2fbd · outbound

This paper cites ACM SIGKDD explorations 44 newsletter 6(1), 20–29 (2004).

Resampling strategies for imbalanced regression: a survey and empirical analysis ACM SIGKDD explorations 44 newsletter 6(1), 20–29 (2004)

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:16.769898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.858957Z digest=sha256:3ce63a3e29ace5716b09bf118b1a66f3ae56c5b78a8c1ee8ba3199db4301f5a9

Observation 6b72e2c7-f710-45cf-b390-809d0711cbcc · outbound

This paper cites Computational Statistics 14(2), 277–292 (1999).

Resampling strategies for imbalanced regression: a survey and empirical analysis Computational Statistics 14(2), 277–292 (1999)

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:16.608090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.904963Z digest=sha256:c3a8d2996953c8fced37de6515c1a2df1ba6eb9f18125a0bb0039846b5df5c1e

Observation 410d59af-7601-4d16-a36a-de40985419b3 · outbound

This paper cites Computational statistics & data analysis 34(2), 165–191 (2000).

Resampling strategies for imbalanced regression: a survey and empirical analysis Computational statistics & data analysis 34(2), 165–191 (2000)

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:16.456902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:13.961498Z digest=sha256:53b83b8e59b4e9c242100e211b4a4c858a2111cd4bab55406b00402670485bf6

Observation bfcd8a26-d4f5-4727-b3d4-41694630113d · outbound

This paper cites In: Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: Fourth International Workshop on Learning with Imbalanced Domains: Theory and Applications, pp

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:16.242182Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.008635Z digest=sha256:35b7c1fb93eb503879fcd254d8974aeb3c1205181c59a7692527a0ee056e3b58

Observation 51706d92-ce2e-4da4-acb2-3d7c5a4e98d8 · outbound

This paper cites Data Mining and Knowledge Discovery 35, 2389–2466 (2021).

Resampling strategies for imbalanced regression: a survey and empirical analysis Data Mining and Knowledge Discovery 35, 2389–2466 (2021)

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:16.019114Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.060604Z digest=sha256:b56cb336868f42380a5567c733e120ba38370f7aa089aace7973928b804bd41d

Observation b1b07596-de30-429f-b55b-acc20241bee0 · outbound

This paper cites In: International Symposium on Intelligent Data Analysis, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: International Symposium on Intelligent Data Analysis, pp

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:15.835380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.139451Z digest=sha256:889c8f0bf60b5269c7453a0ab939f6f8924594542bc9864c24a9435c7efd81f9

Observation d659ce71-35b1-4191-b333-5d47c19c368d · outbound

This paper cites In: European Conference on Prin- ciples of Data Mining and Knowledge Discovery, pp.

Resampling strategies for imbalanced regression: a survey and empirical analysis In: European Conference on Prin- ciples of Data Mining and Knowledge Discovery, pp

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:15.484350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.216325Z digest=sha256:774431aad32e557da46b1da07f7768194f792c4d2fbde415b0b09856f4865e13

Observation 11382152-c9b9-4ef9-82cf-fdc6c2a91568 · outbound

This paper cites International Journal of Data Science and Analytics 3(3), 161–181 (2017).

Resampling strategies for imbalanced regression: a survey and empirical analysis International Journal of Data Science and Analytics 3(3), 161–181 (2017)

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:15.212491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.273385Z digest=sha256:3172c3cae643f3f23884aa4b1e6deda901c83c121da29b62861e9908e58425cb

Observation d8ade955-128f-4d6d-aed8-b4635a33a733 · outbound

This paper cites an unresolved cited work.

Resampling strategies for imbalanced regression: a survey and empirical analysis Unresolved cited work

Reference 53

Resolution
unresolved
raw_fallback, observed 2026-08-06T17:04:15.008220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.340598Z digest=sha256:f210dfadf4a2db318074215cd04df864642eca285659c0b8f293a71e8a8c7dbc

Observation 31921ac2-b634-4ae1-8fec-a37cf1e49f8b · outbound

This paper cites Journal of Machine Learning Research 7(Jan), 1–30 (2006).

Resampling strategies for imbalanced regression: a survey and empirical analysis Journal of Machine Learning Research 7(Jan), 1–30 (2006)

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:14.867861Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.410208Z digest=sha256:c2ca9ab2671a7ad6050aac0be401bf0af96f4cfac17439693b5bf2a0504e406c

Observation 9bda4b57-07c8-441e-84f0-ed54f850d6bc · outbound

This paper cites Machine Learning107(1), 209– 246 (2018) 45.

Resampling strategies for imbalanced regression: a survey and empirical analysis Machine Learning107(1), 209– 246 (2018) 45

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T17:04:14.819447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-06T17:04:14.480768Z digest=sha256:edc8e0be7b7b0824e75a401db2ffc7bf3d9511d41dc29349ea889736bc278654

Pith citing papers

No inbound Pith citation observations are available.