The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.
<p>The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.</p>
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852022847476596736 |
|---|---|
| author | Javad Hemmatian (20700209) |
| author2 | Rassoul Hajizadeh (20700212) Fakhroddin Nazari (20700215) |
| author2_role | author author |
| author_facet | Javad Hemmatian (20700209) Rassoul Hajizadeh (20700212) Fakhroddin Nazari (20700215) |
| author_role | author |
| dc.creator.none.fl_str_mv | Javad Hemmatian (20700209) Rassoul Hajizadeh (20700212) Fakhroddin Nazari (20700215) |
| dc.date.none.fl_str_mv | 2025-02-10T18:25:48Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0317396.g005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/The_methodology_for_dividing_the_samples_of_datasets_for_evaluation_in_classification_methods_a_splitting_the_datasets_into_training_and_test_sets_for_datasets_with_sufficient_samples_b_using_k-fold_cross-validation_for_evaluating_datasets_/28383044 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified using five metrics oversampling minority classes neighbors &# 8217 matthew &# 8217 maternal health risk experimental findings indicate enn methods across cohen &# 8217 category form one become increasingly prominent achieving average improvements smote consistently outperformed smote combines smote smote ), smote smote outperformed rn four imbalanced datasets mcc ), f1 imbalanced data xlink "> two clusters tomek link study proposes results demonstrate recent years proposed method novel data machine learning correlation coefficient classification algorithms |
| dc.title.none.fl_str_mv | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <p>The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_94d67eee47aee252ab8f466d8404a105 |
| identifier_str_mv | 10.1371/journal.pone.0317396.g005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28383044 |
| 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 methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.Javad Hemmatian (20700209)Rassoul Hajizadeh (20700212)Fakhroddin Nazari (20700215)MedicineScience PolicyEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedusing five metricsoversampling minority classesneighbors &# 8217matthew &# 8217maternal health riskexperimental findings indicateenn methods acrosscohen &# 8217category form onebecome increasingly prominentachieving average improvementssmote consistently outperformedsmote combines smotesmote ), smotesmote outperformed rnfour imbalanced datasetsmcc ), f1imbalanced dataxlink ">two clusterstomek linkstudy proposesresults demonstraterecent yearsproposed methodnovel datamachine learningcorrelation coefficientclassification algorithms<p>The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.</p>2025-02-10T18:25:48ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0317396.g005https://figshare.com/articles/figure/The_methodology_for_dividing_the_samples_of_datasets_for_evaluation_in_classification_methods_a_splitting_the_datasets_into_training_and_test_sets_for_datasets_with_sufficient_samples_b_using_k-fold_cross-validation_for_evaluating_datasets_/28383044CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/283830442025-02-10T18:25:48Z |
| spellingShingle | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. Javad Hemmatian (20700209) Medicine Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified using five metrics oversampling minority classes neighbors &# 8217 matthew &# 8217 maternal health risk experimental findings indicate enn methods across cohen &# 8217 category form one become increasingly prominent achieving average improvements smote consistently outperformed smote combines smote smote ), smote smote outperformed rn four imbalanced datasets mcc ), f1 imbalanced data xlink "> two clusters tomek link study proposes results demonstrate recent years proposed method novel data machine learning correlation coefficient classification algorithms |
| status_str | publishedVersion |
| title | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| title_full | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| title_fullStr | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| title_full_unstemmed | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| title_short | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| title_sort | The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples. |
| topic | Medicine Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified using five metrics oversampling minority classes neighbors &# 8217 matthew &# 8217 maternal health risk experimental findings indicate enn methods across cohen &# 8217 category form one become increasingly prominent achieving average improvements smote consistently outperformed smote combines smote smote ), smote smote outperformed rn four imbalanced datasets mcc ), f1 imbalanced data xlink "> two clusters tomek link study proposes results demonstrate recent years proposed method novel data machine learning correlation coefficient classification algorithms |