Paper Citation Record · LEDGER

LoRAS: An oversampling approach for imbalanced datasets

As of 19 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 0 inbound Pith citation observations for arXiv:1908.08346.

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

pith.paper-citation-record.v1
1908.08346 v4

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:46:07.519516Z

measured 44 of 44 standing notices

One-hop event checks from named stored sources.

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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

44 of 44 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 46fc7053-3be1-4856-9d09-a788e2422bf4 · outbound

This paper cites Aditsania , Adiwijaya , and A.

LoRAS: An oversampling approach for imbalanced datasets Aditsania , Adiwijaya , and A

Reference 1

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Observation beb7cebf-14d2-40bb-a589-a67091c22013 · outbound

This paper cites A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis.

LoRAS: An oversampling approach for imbalanced datasets A Study of Synthetic Oversampling for Twitter Imbalanced Sentiment Analysis

Reference 2

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Observation 3dc0ac18-9234-45a3-9f50-35583cc6dd2d · outbound

This paper cites An approach for classification of highly imbalanced data using weighting and undersampling.

LoRAS: An oversampling approach for imbalanced datasets An approach for classification of highly imbalanced data using weighting and undersampling

Reference 3

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Observation 704410e3-83a3-4495-bd88-79f77bef7157 · outbound

This paper cites Monirul Islam, Xin Yao, and Kazuyuki Murase.

LoRAS: An oversampling approach for imbalanced datasets Monirul Islam, Xin Yao, and Kazuyuki Murase

Reference 4

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Observation 2d188c62-aab0-42bf-9b08-cc3868f96cbb · outbound

This paper cites Manifold-based synthetic oversampling with manifold conformance estimation.

LoRAS: An oversampling approach for imbalanced datasets Manifold-based synthetic oversampling with manifold conformance estimation

Reference 5

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This paper cites Beyond the boundaries of smote.

LoRAS: An oversampling approach for imbalanced datasets Beyond the boundaries of smote

Reference 6

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This paper cites Smote for high-dimensional class-imbalanced data.

LoRAS: An oversampling approach for imbalanced datasets Smote for high-dimensional class-imbalanced data

Reference 7

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Observation 6b5fc7fc-7394-49e1-834b-41fe66df56aa · outbound

This paper cites Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem.

LoRAS: An oversampling approach for imbalanced datasets Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem

Reference 8

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 9

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Observation 008ea3f4-d2da-487c-a306-6d5e82feb711 · outbound

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 10

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Observation b7c44d0b-1a6c-4709-9f09-699365339008 · outbound

This paper cites SMOTEBoost: Improving Prediction of the Minority Class in Boosting.

LoRAS: An oversampling approach for imbalanced datasets SMOTEBoost: Improving Prediction of the Minority Class in Boosting

Reference 11

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Observation 84e81962-210b-4eb8-81e5-c53b2b686252 · outbound

This paper cites Extreme anomalous oversampling technique for class imbalance.

LoRAS: An oversampling approach for imbalanced datasets Extreme anomalous oversampling technique for class imbalance

Reference 12

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Observation 6dfab6e7-e1bb-4730-9e8f-8b9beaf82221 · outbound

This paper cites Credit card fraud detection: A realistic modeling and a novel learning strategy.

LoRAS: An oversampling approach for imbalanced datasets Credit card fraud detection: A realistic modeling and a novel learning strategy

Reference 13

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Observation 4e3412cb-1059-432e-a36e-954d648e3b1f · outbound

This paper cites The relationship between precision-recall and roc curves.

LoRAS: An oversampling approach for imbalanced datasets The relationship between precision-recall and roc curves

Reference 14

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Observation b75983e1-f9b0-418e-9997-1c0edad173da · outbound

This paper cites Diversified Ensemble Classifiers for Highly Imbalanced Data Learning and Its Application in Bioinformatics.

LoRAS: An oversampling approach for imbalanced datasets Diversified Ensemble Classifiers for Highly Imbalanced Data Learning and Its Application in Bioinformatics

Reference 15

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Observation 8b4c1b37-b7e3-402b-a74f-7b4030e4d517 · outbound

This paper cites Geometric smote a geometrically enhanced drop-in replacement for smote.

LoRAS: An oversampling approach for imbalanced datasets Geometric smote a geometrically enhanced drop-in replacement for smote

Reference 16

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Observation 112421fc-d78b-4f6e-a43d-11aaee763ffa · outbound

This paper cites Classification of Imbalance Data using Tomek Link (T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method.

LoRAS: An oversampling approach for imbalanced datasets Classification of Imbalance Data using Tomek Link (T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method

Reference 17

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Observation d93aa620-b212-431b-9974-9000089129ce · outbound

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 18

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Observation c5a831a3-3d8b-417b-bfaf-17a346e880d9 · outbound

This paper cites Gosain and S.

LoRAS: An oversampling approach for imbalanced datasets Gosain and S

Reference 19

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Observation fbc90d73-f300-4bc4-9afb-05d847f09d92 · outbound

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

LoRAS: An oversampling approach for imbalanced datasets Adasyn: Adaptive synthetic sampling approach for imbalanced learning

Reference 20

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Observation cdb51c61-a42e-4538-badd-94957c080a55 · outbound

This paper cites Borderline-smote: A new over-sampling method in imbalanced data sets learning.

LoRAS: An oversampling approach for imbalanced datasets Borderline-smote: A new over-sampling method in imbalanced data sets learning

Reference 21

Resolution: verified exact; doi, observed 2026-08-14T11:46:07.717582Z

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Observation 55c825fa-b5e0-4cbd-a297-5ecfc64532a7 · outbound

This paper cites SMOTE Bagging Algorithm for Imbalanced Dataset in Logistic Regression Analysis (Case: Credit of Bank X).

LoRAS: An oversampling approach for imbalanced datasets SMOTE Bagging Algorithm for Imbalanced Dataset in Logistic Regression Analysis (Case: Credit of Bank X)

Reference 22

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Observation 8526bd71-f3c5-42dd-a468-feaa1726c101 · outbound

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 23

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Observation ffb5fe40-b9f1-4c51-b9ee-3bcd6d3b1730 · outbound

This paper cites B2fse framework for high dimensional imbalanced data: A case study for drug toxicity prediction.

LoRAS: An oversampling approach for imbalanced datasets B2fse framework for high dimensional imbalanced data: A case study for drug toxicity prediction

Reference 24

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Observation 38ab05ed-cf29-447d-bac9-a9c4a934fc02 · outbound

This paper cites MSMOTE: Improving Classification Performance when Training Data is imbalanced.

LoRAS: An oversampling approach for imbalanced datasets MSMOTE: Improving Classification Performance when Training Data is imbalanced

Reference 25

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 26

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Observation 2472bc71-7e6b-494b-bcb2-afa2d6490815 · outbound

This paper cites Jing , X.

LoRAS: An oversampling approach for imbalanced datasets Jing , X

Reference 27

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Observation 7841967b-1c8e-4faa-a8c0-0dde70b32ca1 · outbound

This paper cites Visualizing data using t-SNE.

LoRAS: An oversampling approach for imbalanced datasets Visualizing data using t-SNE

Reference 28

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Observation a0b67a9e-be0b-44fe-889e-ae2ed948f3a7 · outbound

This paper cites Smote-variants: A python implementation of 85 minority oversampling techniques.

LoRAS: An oversampling approach for imbalanced datasets Smote-variants: A python implementation of 85 minority oversampling techniques

Reference 29

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Observation 25ef0ec6-0846-4374-8914-ad3c5de26dac · outbound

This paper cites A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction.

LoRAS: An oversampling approach for imbalanced datasets A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction

Reference 30

Resolution: verified exact; doi, observed 2026-08-14T11:46:07.653401Z

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Observation 05d20b3d-ee1b-4103-b6b8-35d724fc1305 · outbound

This paper cites Noisy replication in skewed binary classification.

LoRAS: An oversampling approach for imbalanced datasets Noisy replication in skewed binary classification

Reference 31

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LoRAS: An oversampling approach for imbalanced datasets Unresolved cited work

Reference 32

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This paper cites Abd Elrahman and Ajith Abraham.

LoRAS: An oversampling approach for imbalanced datasets Abd Elrahman and Ajith Abraham

Reference 33

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Observation 8d3fa5dc-a370-4ad8-a49e-05f8bb0c09cb · outbound

This paper cites Kernel-based smote for svm classification of imbalanced datasets.

LoRAS: An oversampling approach for imbalanced datasets Kernel-based smote for svm classification of imbalanced datasets

Reference 34

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Observation c6c6b68c-cfc0-402c-83b3-70b5e1dd8e07 · outbound

This paper cites A pruning-based approach for searching precise and generalized region for synthetic minority over-sampling.

LoRAS: An oversampling approach for imbalanced datasets A pruning-based approach for searching precise and generalized region for synthetic minority over-sampling

Reference 35

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Observation c2e3af4d-b56a-4ca3-bcfb-d37683496d03 · outbound

This paper cites Smote-frst: A new resampling method using fuzzy rough set theory.

LoRAS: An oversampling approach for imbalanced datasets Smote-frst: A new resampling method using fuzzy rough set theory

Reference 36

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Observation f4be2499-580f-4f67-9924-5c2849701b4a · outbound

This paper cites Saez, Bartosz Krawczyk, and Micha Wo\' z niak.

LoRAS: An oversampling approach for imbalanced datasets Saez, Bartosz Krawczyk, and Micha Wo\' z niak

Reference 37

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Observation 401bc48b-635c-4cbd-b364-ccfe5e68c192 · outbound

This paper cites The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets.

LoRAS: An oversampling approach for imbalanced datasets The precision-recall plot is more informative than the roc plot when evaluating binary classifiers on imbalanced datasets

Reference 38

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Observation e16215ab-d76a-494c-a830-b8cdae55a530 · outbound

This paper cites Synthetic over sampling methods for handling class imbalanced problems : A review.

LoRAS: An oversampling approach for imbalanced datasets Synthetic over sampling methods for handling class imbalanced problems : A review

Reference 39

Resolution: verified exact; doi, observed 2026-08-14T11:46:07.588068Z

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Observation 5ffa1390-837f-41a2-8489-82e1c438ad61 · outbound

This paper cites A comparison of oversampling methods on imbalanced topic classification of korean news articles.

LoRAS: An oversampling approach for imbalanced datasets A comparison of oversampling methods on imbalanced topic classification of korean news articles

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:46:08.714533Z

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Observation 3f225928-3ba6-4a7c-b52e-929252c507ab · outbound

This paper cites Visualizing data using t-SNE.

LoRAS: An oversampling approach for imbalanced datasets Visualizing data using t-SNE

Reference 41

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Observation d29e809b-3dfa-48fb-97b6-033210f08cdb · outbound

This paper cites Varmedja , M.

LoRAS: An oversampling approach for imbalanced datasets Varmedja , M

Reference 42

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This paper cites A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients.

LoRAS: An oversampling approach for imbalanced datasets A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients

Reference 43

Resolution: verified exact; doi, observed 2026-08-14T11:46:07.574729Z

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This paper cites Young, Ii, Scott L.

LoRAS: An oversampling approach for imbalanced datasets Young, Ii, Scott L

Reference 44

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Pith citing papers

No inbound Pith citation observations are available.