Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.
<p>Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.</p>
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2025
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| _version_ | 1852015444602388480 |
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| author | Cai Yuanqing (22513351) |
| author2 | Zhenming Gao (11789955) Zhang Jian (9250393) Roohallah Alizadehsani (6445298) Paweł Pławiak (17328063) |
| author2_role | author author author author |
| author_facet | Cai Yuanqing (22513351) Zhenming Gao (11789955) Zhang Jian (9250393) Roohallah Alizadehsani (6445298) Paweł Pławiak (17328063) |
| author_role | author |
| dc.creator.none.fl_str_mv | Cai Yuanqing (22513351) Zhenming Gao (11789955) Zhang Jian (9250393) Roohallah Alizadehsani (6445298) Paweł Pławiak (17328063) |
| dc.date.none.fl_str_mv | 2025-10-28T17:25:50Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0332150.t003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/Performance_comparison_of_the_proposed_model_against_advanced_and_ablated_studies_using_the_CSMAR_dataset_/30469723 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified often fallen short neural network approaches experienced swift growth china stock market based virtual machine 763 %, 86 358 %, 87 traditional models include three major challenges predicting credit risk forecasting credit risk experimental findings demonstrate credit risk prediction csmar ), morningstar optimizing data use listed companies based improved reinforcement learning enhances sample efficiency 047 %, 90 bayesian optimization hyperband reinforcement learning listed companies 576 %, major progression findings validate art models optimization process kvm ), hyperparameter optimization gmsc ), xlink "> significantly speeding regulatory bodies recent years previous works policy updates paper presents kmv default imbalanced classification financial sector feature selection existing state escalating prominence economic settings critical task achieving f accounting research |
| dc.title.none.fl_str_mv | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_d98709d89f61ba48a52c2db22c0f3ecf |
| identifier_str_mv | 10.1371/journal.pone.0332150.t003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30469723 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.Cai Yuanqing (22513351)Zhenming Gao (11789955)Zhang Jian (9250393)Roohallah Alizadehsani (6445298)Paweł Pławiak (17328063)CancerScience PolicyBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedoften fallen shortneural network approachesexperienced swift growthchina stock marketbased virtual machine763 %, 86358 %, 87traditional models includethree major challengespredicting credit riskforecasting credit riskexperimental findings demonstratecredit risk predictioncsmar ), morningstaroptimizing data uselisted companies basedimproved reinforcement learningenhances sample efficiency047 %, 90bayesian optimization hyperbandreinforcement learninglisted companies576 %,major progressionfindings validateart modelsoptimization processkvm ),hyperparameter optimizationgmsc ),xlink ">significantly speedingregulatory bodiesrecent yearsprevious workspolicy updatespaper presentskmv defaultimbalanced classificationfinancial sectorfeature selectionexisting stateescalating prominenceeconomic settingscritical taskachieving faccounting research<p>Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset.</p>2025-10-28T17:25:50ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0332150.t003https://figshare.com/articles/dataset/Performance_comparison_of_the_proposed_model_against_advanced_and_ablated_studies_using_the_CSMAR_dataset_/30469723CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304697232025-10-28T17:25:50Z |
| spellingShingle | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. Cai Yuanqing (22513351) Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified often fallen short neural network approaches experienced swift growth china stock market based virtual machine 763 %, 86 358 %, 87 traditional models include three major challenges predicting credit risk forecasting credit risk experimental findings demonstrate credit risk prediction csmar ), morningstar optimizing data use listed companies based improved reinforcement learning enhances sample efficiency 047 %, 90 bayesian optimization hyperband reinforcement learning listed companies 576 %, major progression findings validate art models optimization process kvm ), hyperparameter optimization gmsc ), xlink "> significantly speeding regulatory bodies recent years previous works policy updates paper presents kmv default imbalanced classification financial sector feature selection existing state escalating prominence economic settings critical task achieving f accounting research |
| status_str | publishedVersion |
| title | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| title_full | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| title_fullStr | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| title_full_unstemmed | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| title_short | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| title_sort | Performance comparison of the proposed model against advanced and ablated studies using the CSMAR dataset. |
| topic | Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified often fallen short neural network approaches experienced swift growth china stock market based virtual machine 763 %, 86 358 %, 87 traditional models include three major challenges predicting credit risk forecasting credit risk experimental findings demonstrate credit risk prediction csmar ), morningstar optimizing data use listed companies based improved reinforcement learning enhances sample efficiency 047 %, 90 bayesian optimization hyperband reinforcement learning listed companies 576 %, major progression findings validate art models optimization process kvm ), hyperparameter optimization gmsc ), xlink "> significantly speeding regulatory bodies recent years previous works policy updates paper presents kmv default imbalanced classification financial sector feature selection existing state escalating prominence economic settings critical task achieving f accounting research |