Paper Citation Record · LEDGER

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks

As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 0 inbound Pith citation observations for arXiv:2507.21119.

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

pith.paper-citation-record.v1
2507.21119 v1

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:31:07.612731Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

23 of 23 outbound references displayed

verified exact8
verified fuzzy4
unresolved5
parse uncertain0
malformed identifier4
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d4d81a1-0001-44e1-872a-b8d648612df8 · outbound

This paper cites A tutorial on machine learning for failure management in optical networks.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks A tutorial on machine learning for failure management in optical networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:31:10.747085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 1735cb05-2c43-4016-baaf-e7e5589ddef9 · outbound

This paper cites Addressing data scarcity in ml-based failure-cause identification in opti- cal networks through generative models.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Addressing data scarcity in ml-based failure-cause identification in opti- cal networks through generative models

Reference 2

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:31:06.076059Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8f522fa9-b4d2-48ec-90fd-3e87f6339f20 · outbound

This paper cites Data augmentation to improve perfor- mance of neural networks for failure management in optical networks.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Data augmentation to improve perfor- mance of neural networks for failure management in optical networks

Reference 3

Resolution: verified exact; doi, observed 2026-08-06T16:31:08.689399Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0fb01148-27fc-4a37-8486-00b280e681fc · outbound

This paper cites A gan based soft fail- ure detection and identification framework for long-haul coherent optical communication systems.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks A gan based soft fail- ure detection and identification framework for long-haul coherent optical communication systems

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:31:10.623239Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 07546e8e-7773-48f9-a4f6-18e088e0348e · outbound

This paper cites Data augmentation to reduce compu- tational complexity of neural-network-based soft-failure cause identifier.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Data augmentation to reduce compu- tational complexity of neural-network-based soft-failure cause identifier

Reference 5

Resolution: verified exact; doi, observed 2026-08-06T16:31:08.531328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8e0a8a67-a2b8-4141-b874-7960a17af68d · outbound

This paper cites Failure data augmenta- tion for optical network equipment using time-series gen- erative adversarial networks.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Failure data augmenta- tion for optical network equipment using time-series gen- erative adversarial networks

Reference 6

Resolution: verified exact; doi, observed 2026-08-06T16:31:08.365743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8ced9c4d-5c9e-42cb-958c-900a79e6e4cb · outbound

This paper cites The potential of data augmentation for failure management in optical networks.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks The potential of data augmentation for failure management in optical networks

Reference 7

Resolution: verified exact; doi, observed 2026-08-06T16:31:08.235835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ddbdf4fe-57b1-40ce-bb36-909cbc69df1a · outbound

This paper cites Model and data-centric machine learn- ing algorithms to address data scarcity for failure iden- tification.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Model and data-centric machine learn- ing algorithms to address data scarcity for failure iden- tification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:31:10.472493Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a1d15471-53e9-4054-8ae8-0ffd11f1a8a2 · outbound

This paper cites Monitoring data augmentation of spectral information using vae and gan for soft-failure identification.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Monitoring data augmentation of spectral information using vae and gan for soft-failure identification

Reference 9

Resolution: verified exact; doi, observed 2026-08-06T16:31:08.104116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7df571e6-054a-4720-8a39-5c6918627ec5 · outbound

This paper cites Shap-assisted ee-lightgbm model for explainable fault diagnosis in practical optical networks.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Shap-assisted ee-lightgbm model for explainable fault diagnosis in practical optical networks

Reference 10

Resolution: verified exact; doi, observed 2026-08-06T16:31:07.980772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b78860c6-7263-481b-82fc-c805c4bb46ff · outbound

This paper cites Application of ml algorithms for prediction of the qot in optical net- works with imbalanced and incomplete data.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Application of ml algorithms for prediction of the qot in optical net- works with imbalanced and incomplete data

Reference 11

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:31:09.520539Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b213c3ba-df7c-4b61-94e9-2bdfc32965b6 · outbound

This paper cites A stacking ensemble ml-based failure prediction model for optical networks with imbalanced data.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks A stacking ensemble ml-based failure prediction model for optical networks with imbalanced data

Reference 12

Resolution: malformed identifier; doi_truncated, observed 2026-08-06T16:31:09.298592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fc21ffac-f9be-43c1-9683-60643d68cb19 · outbound

This paper cites Survey on machine learning biases and mitigation techniques.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Survey on machine learning biases and mitigation techniques

Reference 13

Resolution: verified exact; doi, observed 2026-08-06T16:31:07.815560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b966a1bb-5244-4f04-bcab-84bb3b1ea876 · outbound

This paper cites Bias mitigation for machine learning classifiers: A com- prehensive survey.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Bias mitigation for machine learning classifiers: A com- prehensive survey

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:31:10.353893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b86332f1-c43e-4b1a-a66b-50349c19db5f · outbound

This paper cites Smote: Synthetic minority over-sampling technique.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Smote: Synthetic minority over-sampling technique

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T16:31:07.145683Z

Source-reported events for the cited work

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Observation b0f0c90b-f5d8-4ae9-ae1a-9247b0d28624 · outbound

This paper cites Adasyn: Adaptive synthetic sampling approach for imbalanced learning.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Adasyn: Adaptive synthetic sampling approach for imbalanced learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T16:31:07.211212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7eaafd79-c32c-4270-8c5c-53e4558ed04b · outbound

This paper cites A study of the behavior of several methods for balancing ma- chine learning training data.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks A study of the behavior of several methods for balancing ma- chine learning training data

Reference 17

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:31:07.311493Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7880709-4b72-4f1d-a2dc-0c9009a5e276 · outbound

This paper cites Modeling Tabular data using Conditional GAN.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Modeling Tabular data using Conditional GAN

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-06T16:31:07.380146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 47be06f3-6103-4a9e-85e0-b422b7858da3 · outbound

This paper cites Exploratory undersam- pling for class-imbalance learning.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Exploratory undersam- pling for class-imbalance learning

Reference 19

Resolution: malformed identifier; no resolver link, observed 2026-08-06T16:31:07.445205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3f97a258-ba87-40f6-9cb0-a081f170c4bb · outbound

This paper cites an unresolved cited work.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Unresolved cited work

Reference 20

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:31:10.239244Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9ba4a009-2a53-40c6-8ee7-45dd603449fd · outbound

This paper cites Learning long-and short-term temporal patterns for ml- driven fault management in optical communication net- works.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Learning long-and short-term temporal patterns for ml- driven fault management in optical communication net- works

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:31:07.612731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f332294-6ec7-4e5c-b73b-86aed94fb72a · outbound

This paper cites an unresolved cited work.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Unresolved cited work

Reference 2019

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:31:10.082516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 5f9dcb71-b178-4a99-b5b2-819a79eca964 · outbound

This paper cites an unresolved cited work.

Pre-, In-, and Post-Processing Class Imbalance Mitigation Techniques for Failure Detection in Optical Networks Unresolved cited work

Reference 2023

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:31:09.802723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.