The parameter settings of the RF algorithm after parameter optimization.
<p>The parameter settings of the RF algorithm after parameter optimization.</p>
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2025
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| _version_ | 1852022175123374080 |
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| author | Xini Fang (20861990) |
| author_facet | Xini Fang (20861990) |
| author_role | author |
| dc.creator.none.fl_str_mv | Xini Fang (20861990) |
| dc.date.none.fl_str_mv | 2025-03-11T17:42:04Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0318491.t001 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/The_parameter_settings_of_the_RF_algorithm_after_parameter_optimization_/28577165 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Immunology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified provides basic information experimental results show early warning strategies operational risk theory human resource risk enterprise risk assessment model &# 8217 45 %, 5 45 %, 4 09 %, 4 processing sample data handle complex data corporate operational risk traditional rf model improved rf model fcm clustering algorithm clustering algorithm rf algorithm risk indicators market risk financial risk development risk model achieves 95 %, 81 %, 29 %, 26 %, 20 %, rating data performance data fcm clustering data classification corporate bonds xlink "> weight method thereby enhancing response speed respectively 6 random forest primary indicators net value intercriteria correlation income products fuzzy c f1 score 48 %. |
| dc.title.none.fl_str_mv | The parameter settings of the RF algorithm after parameter optimization. |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>The parameter settings of the RF algorithm after parameter optimization.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_4bbbdfb7f027317841cf2bc4b9fb6eb8 |
| identifier_str_mv | 10.1371/journal.pone.0318491.t001 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28577165 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | The parameter settings of the RF algorithm after parameter optimization.Xini Fang (20861990)BiotechnologyImmunologyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedprovides basic informationexperimental results showearly warning strategiesoperational risk theoryhuman resource riskenterprise risk assessmentmodel &# 821745 %, 545 %, 409 %, 4processing sample datahandle complex datacorporate operational risktraditional rf modelimproved rf modelfcm clustering algorithmclustering algorithmrf algorithmrisk indicatorsmarket riskfinancial riskdevelopment riskmodel achieves95 %,81 %,29 %,26 %,20 %,rating dataperformance datafcm clusteringdata classificationcorporate bondsxlink ">weight methodthereby enhancingresponse speedrespectively 6random forestprimary indicatorsnet valueintercriteria correlationincome productsfuzzy cf1 score48 %.<p>The parameter settings of the RF algorithm after parameter optimization.</p>2025-03-11T17:42:04ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0318491.t001https://figshare.com/articles/dataset/The_parameter_settings_of_the_RF_algorithm_after_parameter_optimization_/28577165CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/285771652025-03-11T17:42:04Z |
| spellingShingle | The parameter settings of the RF algorithm after parameter optimization. Xini Fang (20861990) Biotechnology Immunology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified provides basic information experimental results show early warning strategies operational risk theory human resource risk enterprise risk assessment model &# 8217 45 %, 5 45 %, 4 09 %, 4 processing sample data handle complex data corporate operational risk traditional rf model improved rf model fcm clustering algorithm clustering algorithm rf algorithm risk indicators market risk financial risk development risk model achieves 95 %, 81 %, 29 %, 26 %, 20 %, rating data performance data fcm clustering data classification corporate bonds xlink "> weight method thereby enhancing response speed respectively 6 random forest primary indicators net value intercriteria correlation income products fuzzy c f1 score 48 %. |
| status_str | publishedVersion |
| title | The parameter settings of the RF algorithm after parameter optimization. |
| title_full | The parameter settings of the RF algorithm after parameter optimization. |
| title_fullStr | The parameter settings of the RF algorithm after parameter optimization. |
| title_full_unstemmed | The parameter settings of the RF algorithm after parameter optimization. |
| title_short | The parameter settings of the RF algorithm after parameter optimization. |
| title_sort | The parameter settings of the RF algorithm after parameter optimization. |
| topic | Biotechnology Immunology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified provides basic information experimental results show early warning strategies operational risk theory human resource risk enterprise risk assessment model &# 8217 45 %, 5 45 %, 4 09 %, 4 processing sample data handle complex data corporate operational risk traditional rf model improved rf model fcm clustering algorithm clustering algorithm rf algorithm risk indicators market risk financial risk development risk model achieves 95 %, 81 %, 29 %, 26 %, 20 %, rating data performance data fcm clustering data classification corporate bonds xlink "> weight method thereby enhancing response speed respectively 6 random forest primary indicators net value intercriteria correlation income products fuzzy c f1 score 48 %. |