Paper Citation Record · LEDGER

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach

As of 13 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.17128.

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

pith.paper-citation-record.v1
2411.17128 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:33:18.203324Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+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

36 of 36 outbound references displayed

verified exact3
verified fuzzy27
unresolved4
parse uncertain0
malformed identifier1
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 392824c5-cf13-4ce8-8aeb-1c0d676b13da · outbound

This paper cites Machine learning driven extended matrix norm method for the solution of large-scale zero-sum matrix games,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Machine learning driven extended matrix norm method for the solution of large-scale zero-sum matrix games,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.320575Z

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Observation d7f9a710-7103-4d97-bc39-b546ca95505f · outbound

This paper cites Signal propagation in complex networks,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Signal propagation in complex networks,

Reference 2

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Observation 5908b925-df79-44b1-9494-0f4d67497dc9 · outbound

This paper cites Machine learning partners in criminal networks,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Machine learning partners in criminal networks,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.294308Z

Source-reported events for the cited work

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Observation 70199db0-a9c1-4a53-8e1b-a91035108a34 · outbound

This paper cites Machine learning techniques for the diagnosis of Alzheimer’s disease: A review,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Machine learning techniques for the diagnosis of Alzheimer’s disease: A review,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.280302Z

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Observation b1ac294b-eed7-446e-a084-a4520d713ba1 · outbound

This paper cites Ensemble deep learning for Alzheimer’s disease characterization and estimation,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Ensemble deep learning for Alzheimer’s disease characterization and estimation,

Reference 5

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Observation 397ee855-5a5a-42aa-854b-b44301695d97 · outbound

This paper cites Fuzzy deep learning for the diagnosis of Alzheimer’s disease: Approaches and challenges,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Fuzzy deep learning for the diagnosis of Alzheimer’s disease: Approaches and challenges,

Reference 6

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Source-reported events for the cited work

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Observation 28cfdf42-adc1-4878-8248-1d840590a9db · outbound

This paper cites Support-vector networks,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Support-vector networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.252134Z

Source-reported events for the cited work

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Observation 91698f01-e13f-4058-85ac-8ca27a1a8adf · outbound

This paper cites RoBoSS: A robust, bounded, sparse, and smooth loss function for supervised learning,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach RoBoSS: A robust, bounded, sparse, and smooth loss function for supervised learning,

Reference 8

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Source-reported events for the cited work

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Observation 124aba1e-cdf8-45e0-a9b8-ab5b9dfd6faf · outbound

This paper cites Ad- vancing supervised learning with the wave loss function: A robust and smooth approach,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Ad- vancing supervised learning with the wave loss function: A robust and smooth approach,

Reference 9

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 10022171-af73-48b2-b680-dbe61998ed4a · outbound

This paper cites GL-TSVM: A robust and smooth twin support vector machine with guardian loss function.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach GL-TSVM: A robust and smooth twin support vector machine with guardian loss function

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-08-12T12:33:18.262294Z

Source-reported events for the cited work

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Observation 9c4b4948-f60b-45bc-8652-3ad8bc02de86 · outbound

This paper cites Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Enhancing Multiview Synergy: Robust Learning by Exploiting the Wave Loss Function with Consensus and Complementarity Principles

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-08-12T12:33:18.428794Z

Source-reported events for the cited work

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Observation 220970c6-17e6-4796-8a53-ab5e2c4d55c9 · outbound

This paper cites Diagnosis of breast cancer using flexible pinball loss support vector machine,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Diagnosis of breast cancer using flexible pinball loss support vector machine,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.238546Z

Source-reported events for the cited work

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Observation ef83c491-aecd-4081-a535-d325e21acf91 · outbound

This paper cites Learning from imbalanced data: open challenges and future directions,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Learning from imbalanced data: open challenges and future directions,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.224624Z

Source-reported events for the cited work

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Observation 0cdeff22-7945-4967-81b6-52d6bbbacedd · outbound

This paper cites Clas- sification of imbalanced data by oversampling in kernel space of support vector machines,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Clas- sification of imbalanced data by oversampling in kernel space of support vector machines,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.210623Z

Source-reported events for the cited work

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Observation cd86010b-3aaf-4cc6-bcb8-2f2ce7c6a8e7 · outbound

This paper cites Ma- chine learning with oversampling and undersampling techniques: overview study and experimental results,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Ma- chine learning with oversampling and undersampling techniques: overview study and experimental results,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.196913Z

Source-reported events for the cited work

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

This paper cites Survey of resampling techniques for improving classification performance in unbalanced datasets.

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

Reference 16

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Source-reported events for the cited work

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Observation 8ddd414d-7941-465c-8c14-de693b76b1dc · outbound

This paper cites Neural network with absent minority class samples and boundary shifting for imbalanced data classification,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Neural network with absent minority class samples and boundary shifting for imbalanced data classification,

Reference 17

Resolution: verified exact; doi, observed 2026-08-12T12:33:18.240460Z

Source-reported events for the cited work

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Observation 35349a5d-af90-4e47-bf04-08af996e6035 · outbound

This paper cites Cost-sensitive learning methods for imbalanced data,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Cost-sensitive learning methods for imbalanced data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.184207Z

Source-reported events for the cited work

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Observation 1b0b7e8d-da6f-422b-8863-1726dadcc0e2 · outbound

This paper cites Support vector machine-based optimized de- cision threshold adjustment strategy for classifying im- balanced data,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Support vector machine-based optimized de- cision threshold adjustment strategy for classifying im- balanced data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.170405Z

Source-reported events for the cited work

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Observation fb9dd587-92db-4a0e-82ce-ee79fdf94bbc · outbound

This paper cites Imbalanced 10 data classification based on scaling kernel-based support vector machine,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Imbalanced 10 data classification based on scaling kernel-based support vector machine,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.156508Z

Source-reported events for the cited work

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Observation 5ac8c41e-f132-4e4c-a68f-02b365d969b8 · outbound

This paper cites Con- trolling the sensitivity of support vector machines,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Con- trolling the sensitivity of support vector machines,

Reference 21

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Source-reported events for the cited work

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Observation 953a08fb-08cb-436a-a50d-8dd1c37c0d41 · outbound

This paper cites Fuzzy support vector ma- chines,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Fuzzy support vector ma- chines,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.127624Z

Source-reported events for the cited work

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Observation b98f5955-d8ab-4eba-bb99-9d81690d99fe · outbound

This paper cites FSVM-CIL: fuzzy support vector machines for class imbalance learning,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach FSVM-CIL: fuzzy support vector machines for class imbalance learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.113288Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2c3668db-2501-4d49-b7ec-e5d723ae90a5 · outbound

This paper cites Slack-factor-based fuzzy support vector machine for class imbalance problems,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Slack-factor-based fuzzy support vector machine for class imbalance problems,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.098884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 295d7402-f7fe-4c22-b04c-e9d92b5f8ddc · outbound

This paper cites Hashing-based undersampling ensemble for imbalanced pattern classification problems,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Hashing-based undersampling ensemble for imbalanced pattern classification problems,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.084362Z

Source-reported events for the cited work

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Observation bbbf188b-9b18-4da0-989b-13b0037367d4 · outbound

This paper cites Centered kernel alignment inspired fuzzy support vector machine,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Centered kernel alignment inspired fuzzy support vector machine,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.070091Z

Source-reported events for the cited work

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Observation 01ce8b5f-ad30-40ca-91cc-4831b13e3667 · outbound

This paper cites Tackling the poor assumptions of naive bayes text classification. machine learning,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Tackling the poor assumptions of naive bayes text classification. machine learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.055190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4560a165-2099-4b2a-9243-dbb36d02e3ac · outbound

This paper cites Adaptive SV- borderline SMOTE-SVM algorithm for imbalanced data classification,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Adaptive SV- borderline SMOTE-SVM algorithm for imbalanced data classification,

Reference 28

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Source-reported events for the cited work

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Observation b155e11e-d8b8-46e4-bbc3-3f1efcead1fd · outbound

This paper cites New oversampling ap- proaches based on polynomial fitting for imbalanced data sets,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach New oversampling ap- proaches based on polynomial fitting for imbalanced data sets,

Reference 29

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Observation 31f03077-8632-46b6-bd61-1624471fc33c · outbound

This paper cites A study of the behavior of several methods for balancing machine learning training data,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach A study of the behavior of several methods for balancing machine learning training data,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:19.010443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation db561a02-d147-4369-a97b-aa8a899a57a3 · outbound

This paper cites MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach MWMOTE–majority weighted minority oversampling technique for imbalanced data set learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:18.994722Z

Source-reported events for the cited work

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Observation d546a6dd-4ada-4569-8cb2-97e9df6b72cd · outbound

This paper cites KEEL data-mining software tool: Data set repository, integra- tion of algorithms and experimental analysis framework,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach KEEL data-mining software tool: Data set repository, integra- tion of algorithms and experimental analysis framework,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:18.979928Z

Source-reported events for the cited work

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Observation 162d2bfb-bfdd-4e68-8cb9-13a112167e8f · outbound

This paper cites An over-sampling expert system for learing from imbalanced data sets,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach An over-sampling expert system for learing from imbalanced data sets,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:18.965091Z

Source-reported events for the cited work

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Observation 8ee1ee11-32a2-420e-8655-b51c665bb618 · outbound

This paper cites Learning with mitigating random consistency from the accuracy measure,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Learning with mitigating random consistency from the accuracy measure,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:18.950777Z

Source-reported events for the cited work

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Observation c7e11c6e-0013-4aeb-879e-f09aca1bc6a3 · outbound

This paper cites Generalization performance of pure accuracy and its application in selective ensemble learning,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Generalization performance of pure accuracy and its application in selective ensemble learning,

Reference 35

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Source-reported events for the cited work

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Observation e863d823-36ac-4a3a-b67e-530a6993f8b5 · outbound

This paper cites Intuitionistic fuzzy weighted least squares twin SVMs,.

Enhancing Imbalance Learning: A Novel Slack-Factor Fuzzy SVM Approach Intuitionistic fuzzy weighted least squares twin SVMs,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:33:18.936131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Pith citing papers

No inbound Pith citation observations are available.