Paper Citation Record · LEDGER

Survey of resampling techniques for improving classification performance in unbalanced datasets

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1608.06048.

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

pith.paper-citation-record.v1
1608.06048 v1

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 2026-08-19T06:32:44.657259+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:16:41.314135Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

179
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2ba22b04-12db-451a-9c01-9f922a277175 · inbound

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification cites this paper.

Adaptive Ensemble of Classifiers with Regularization for Imbalanced Data Classification Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-14T14:16:41.314135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea073460-6b88-4c23-ad67-238c5340ca92 · inbound

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach cites this paper.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T12:33:18.112891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 294b10ab-665a-44b3-9f7f-a9bd479d0677 · inbound

Emotion estimation from video footage with LSTM cites this paper.

Emotion estimation from video footage with LSTM Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T16:11:55.502645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65557623-4e02-41e6-874a-8c0b3539b4b2 · inbound

Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration cites this paper.

Long-tailed Medical Diagnosis with Relation-aware Representation Learning and Iterative Classifier Calibration Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-09T05:28:43.934833Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3f0dbee-270b-4434-9642-f7f003c9be92 · inbound

On the Burstiness of Faces in Set cites this paper.

On the Burstiness of Faces in Set Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T22:54:54.243869Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f073b76c-3089-4a36-b039-7b25fb3167b6 · inbound

Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints cites this paper.

Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints Survey of resampling techniques for improving classification performance in unbalanced datasets

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-03T07:53:28.540512Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.