Paper Citation Record · LEDGER

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost

As of 16 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:1908.01672.

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

pith.paper-citation-record.v1
1908.01672 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:13:01.207054Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

41 of 41 outbound references displayed

verified exact1
verified fuzzy29
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7c3966f5-3eb2-4a7d-9e5a-97ed6aa68a0a · outbound

This paper cites Xgboost: A scalable tree boosting system.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Xgboost: A scalable tree boosting system

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.015024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e81b9d3c-9a6c-485e-b01a-ac0bc52975c3 · outbound

This paper cites A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost A benchmark for comparing precision medicine methods in thyroid cancer diagnosis using tissue microarrays

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.805263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3ea52c8-df33-4d73-8d85-fedba4de8f61 · outbound

This paper cites Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data

Reference 3

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:13:01.254212Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1997c6b-c57a-4c86-9688-2b6328b3545c · outbound

This paper cites Application of extreme gradient boosting trees in the construction of credit risk assessment models for ﬁnancial institutions.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Application of extreme gradient boosting trees in the construction of credit risk assessment models for ﬁnancial institutions

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.789822Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2db5b80a-8fe1-4430-ac3d-71e15c69b0b2 · outbound

This paper cites A comprehensive study on predicting functional role of metagenomes using machine learning methods.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost A comprehensive study on predicting functional role of metagenomes using machine learning methods

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.771649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3d8b8302-262a-42de-8cd5-37324e117c25 · outbound

This paper cites Xgboost and lgbm for porto seguro’s kaggle challenge: A comparison.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Xgboost and lgbm for porto seguro’s kaggle challenge: A comparison

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.756306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b629c118-0323-41ed-ad6f-e2a00e785c88 · outbound

This paper cites Tree boosting with xgboost-why does xgboost win "every" machine learning competition? Master’s thesis, NTNU, 2016.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Tree boosting with xgboost-why does xgboost win "every" machine learning competition? Master’s thesis, NTNU, 2016

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.739734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 919d55d1-99a5-4530-a94c-2fd4adbe7ed8 · outbound

This paper cites Imbalance learning for the prediction of n 6-methylation sites in mrnas.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Imbalance learning for the prediction of n 6-methylation sites in mrnas

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.724737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9a878acb-ff9f-4313-849b-79fa2adf57d8 · outbound

This paper cites Bagging of xgboost classiﬁers with random under-sampling and tomek link for noisy label-imbalanced data.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Bagging of xgboost classiﬁers with random under-sampling and tomek link for noisy label-imbalanced data

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.706937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9d6d8722-23ac-4e64-ad89-ae1d421bfbac · outbound

This paper cites Classiﬁcation of imbalanced data: A review.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Classiﬁcation of imbalanced data: A review

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.689531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation af63fc91-fc1b-4c84-9e31-2c64b5fd21c5 · outbound

This paper cites Learning deep representation for imbalanced classiﬁcation.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Learning deep representation for imbalanced classiﬁcation

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.671700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c24b8258-c3a1-4061-98ac-a83cb05d988f · outbound

This paper cites Focal loss for dense object detection.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Focal loss for dense object detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.067560Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1019b6d3-ec7e-49a3-bbcd-58ef735b8df0 · outbound

This paper cites Classiﬁcation of breast cancer risk factors using several resampling approaches.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Classiﬁcation of breast cancer risk factors using several resampling approaches

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.647401Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f7d0200-e938-429e-8b27-60e0a09494e0 · outbound

This paper cites Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Cost-sensitive boosted tree for loan evaluation in peer-to-peer lending

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.632466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c789831d-d793-4076-91f9-8e82400143fd · outbound

This paper cites Radar emitter classiﬁcation for large data set based on weighted-xgboost.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Radar emitter classiﬁcation for large data set based on weighted-xgboost

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.620234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bfaef3e3-3628-482f-aa68-8b94320e81f8 · outbound

This paper cites The numpy array: a structure for efﬁcient numerical computation.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost The numpy array: a structure for efﬁcient numerical computation

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.606533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 77133716-4838-449b-b800-9b9c8ff568e8 · outbound

This paper cites Scikit-learn: Machine learning in python.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Scikit-learn: Machine learning in python

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.091531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 816a084e-329d-4b93-91cb-9c37cf4ebe2f · outbound

This paper cites pandas: a foundational python library for data analysis and statistics.Python for High Performance and Scientiﬁc Computing, 14, 2011.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost pandas: a foundational python library for data analysis and statistics.Python for High Performance and Scientiﬁc Computing, 14, 2011

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.582342Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2aaaa2ef-074c-4ad0-97c0-cf5ed3ed94fc · outbound

This paper cites Comparison of the predicted and observed secondary structure of t4 phage lysozyme.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Comparison of the predicted and observed secondary structure of t4 phage lysozyme

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.101041Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48a98679-7ce6-4262-aa1e-641f33a30c13 · outbound

This paper cites Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.558363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8acf8a82-e65c-4840-994b-ff1a969b4139 · outbound

This paper cites Reducing multiclass to binary: A unifying approach for margin classiﬁers.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Reducing multiclass to binary: A unifying approach for margin classiﬁers

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.109504Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7807ca19-8227-4804-ace2-f9ce898a96f3 · outbound

This paper cites Multiclass boosting for weak classiﬁers.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Multiclass boosting for weak classiﬁers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.529317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2abb8cbf-7963-44d7-abde-5be6b1aa5c63 · outbound

This paper cites Greedy function approximation: a gradient boosting machine.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Greedy function approximation: a gradient boosting machine

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.120038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7433eddb-07cc-4da4-9166-2ca7086e258f · outbound

This paper cites Gradient boosting machines, a tutorial.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Gradient boosting machines, a tutorial

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.125114Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed4fceef-2778-41c4-9af8-d12f67e391dc · outbound

This paper cites Parallel boosted regression trees for web search ranking.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Parallel boosted regression trees for web search ranking

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.486295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 23019fd7-d359-4dbe-ac8d-2ac7d856db45 · outbound

This paper cites Lightgbm: A highly efﬁcient gradient boosting decision tree.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Lightgbm: A highly efﬁcient gradient boosting decision tree

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.135536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ed55ba2-4e60-4d5a-adca-15e0faebad6c · outbound

This paper cites Catboost: unbiased boosting with categorical features.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Catboost: unbiased boosting with categorical features

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.460706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75b34c4b-c4a9-4b60-8176-5c0723a4c4b4 · outbound

This paper cites Focal loss dense detector for vehicle surveillance.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Focal loss dense detector for vehicle surveillance

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.447216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b26a526b-89af-4fc2-9cb5-ed12096a5db3 · outbound

This paper cites Deepglobe 2018: A challenge to parse the earth through satellite images.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Deepglobe 2018: A challenge to parse the earth through satellite images

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.431806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d8680242-3f9f-44b6-92aa-8945d2656e8f · outbound

This paper cites A feature enriching object detection framework with weak segmentation loss.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost A feature enriching object detection framework with weak segmentation loss

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.418331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 096435d0-00db-4d4e-9b90-c19d099897fd · outbound

This paper cites A novel ensemble method for credit scoring: Adaption of different imbalance ratios.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost A novel ensemble method for credit scoring: Adaption of different imbalance ratios

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.404418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 20865838-3cf7-41e9-b151-9d8af5c5c631 · outbound

This paper cites Exploratory undersampling for class-imbalance learning.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Exploratory undersampling for class-imbalance learning

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-14T15:13:01.164760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2210f551-b3e5-402f-a612-cb95bb67e36b · outbound

This paper cites Tf boosted trees: A scalable tensorﬂow based framework for gradient boosting.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Tf boosted trees: A scalable tensorﬂow based framework for gradient boosting

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.379156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dea81e64-988e-4366-9842-d0cb65a87e16 · outbound

This paper cites Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.365880Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bba4dbdc-005a-4b53-b23c-e54767c64c63 · outbound

This paper cites Rose: A package for binary imbalanced learning.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Rose: A package for binary imbalanced learning

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.351165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f8a3b152-8b30-4867-af6b-9eb4eb9f2f4f · outbound

This paper cites Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Keel data-mining software tool: data set repository, integration of algorithms and experimental analysis framework

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.337745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 100de41e-c70c-480e-92e5-596f6806d655 · outbound

This paper cites The weka data mining software: an update.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost The weka data mining software: an update

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.323689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e2b67f4b-1510-4068-a750-137473aad9c9 · outbound

This paper cites Multi-imbalance: An open-source software for multi-class imbalance learning.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Multi-imbalance: An open-source software for multi-class imbalance learning

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.310544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6eac75d1-978e-4bf4-81ed-e2dbcad6f23c · outbound

This paper cites an unresolved cited work.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:13:01.297427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b54613f7-28e0-4638-903b-fb9e24c08fe5 · outbound

This paper cites an unresolved cited work.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Unresolved cited work

Reference 40

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:13:01.284298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0e9bf474-05e3-4c53-8d61-800252061798 · outbound

This paper cites Tutorial on practical tips of the most inﬂuential data preprocessing algorithms in data mining.

Imbalance-XGBoost: Leveraging Weighted and Focal Losses for Binary Label-Imbalanced Classification with XGBoost Tutorial on practical tips of the most inﬂuential data preprocessing algorithms in data mining

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:13:01.270526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.