Paper Citation Record · LEDGER

Oversampling for Imbalanced Learning Based on K-Means and SMOTE

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1711.00837.

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

pith.paper-citation-record.v1
1711.00837 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:56:16.140034Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T21:10:09.199755Z

Reference resolution

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8b7d0dac-f7e7-4e3a-829b-5b76c7aa5ea0 · inbound

Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification cites this paper.

Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-11T22:33:29.786368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 503ef6e0-9abe-41cf-896b-41f61ff01242 · inbound

Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification cites this paper.

Deep Learning Meets Oversampling: A Learning Framework to Handle Imbalanced Classification Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T18:53:12.444696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 995b15b6-0fdd-418d-92fa-36228a4a9093 · inbound

Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds cites this paper.

Dusty stellar sources classification by implementing machine learning methods based on spectroscopic observations in the Magellanic Clouds Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-16T11:56:16.140034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9131c20d-f2ec-4ea5-a71a-6ccc680ff8c0 · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:32:06.399991Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e5b3013c-8f66-4d80-b772-fb5aa5e4fcfe · inbound

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection cites this paper.

LiBaGS: Lightweight Boundary Gap Synthesis for Targeted Synthetic Data Selection Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-14T20:52:58.614430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 87713041-3a31-4eb0-8244-10c4b1bea1fc · inbound

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? cites this paper.

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? Oversampling for Imbalanced Learning Based on K-Means and SMOTE

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:10:09.201176Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.