Oversampling techniques for imbalanced data in regression
<p>Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression...
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2024
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| _version_ | 1864513509810765824 |
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| author | Samir Brahim Belhaouari (9427347) |
| author2 | Ashhadul Islam (16869981) Khelil Kassoul (18441114) Ala Al-Fuqaha (4434340) Abdesselam Bouzerdoum (17900021) |
| author2_role | author author author author |
| author_facet | Samir Brahim Belhaouari (9427347) Ashhadul Islam (16869981) Khelil Kassoul (18441114) Ala Al-Fuqaha (4434340) Abdesselam Bouzerdoum (17900021) |
| author_role | author |
| dc.creator.none.fl_str_mv | Samir Brahim Belhaouari (9427347) Ashhadul Islam (16869981) Khelil Kassoul (18441114) Ala Al-Fuqaha (4434340) Abdesselam Bouzerdoum (17900021) |
| dc.date.none.fl_str_mv | 2024-05-20T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.eswa.2024.124118 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Oversampling_techniques_for_imbalanced_data_in_regression/26404000 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Data management and data science Machine learning Data augmentation Machine learning AutoInflaters Nearest neighbor Imbalanced data |
| dc.title.none.fl_str_mv | Oversampling techniques for imbalanced data in regression |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (KNNOR-DeepReg) for high-population datasets. For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. For image datasets, we employ Multi-Level Autoencoders, consisting of Convolutional and Fully Connected Autoencoders. For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. The outcomes revealed a positive impact of data augmentation, with a success rate of 83.75% for Light Gradient Boosting Method (LightGBM) and 71.57% for the 18 other regressors employed in the study. This success rate is determined by the frequency of instances where models performed better when augmented data was used compared to instances with no augmentation. Access to the comparative code can be found in GitHub.</p><h2>Other Information</h2> <p> Published in: Expert Systems with Applications<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2024.124118" target="_blank">https://dx.doi.org/10.1016/j.eswa.2024.124118</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_17a4dc6d4a13b009086633bb36e6b54b |
| identifier_str_mv | 10.1016/j.eswa.2024.124118 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26404000 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Oversampling techniques for imbalanced data in regressionSamir Brahim Belhaouari (9427347)Ashhadul Islam (16869981)Khelil Kassoul (18441114)Ala Al-Fuqaha (4434340)Abdesselam Bouzerdoum (17900021)Information and computing sciencesData management and data scienceMachine learningData augmentationMachine learningAutoInflatersNearest neighborImbalanced data<p>Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (KNNOR-DeepReg) for high-population datasets. For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. For image datasets, we employ Multi-Level Autoencoders, consisting of Convolutional and Fully Connected Autoencoders. For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. The outcomes revealed a positive impact of data augmentation, with a success rate of 83.75% for Light Gradient Boosting Method (LightGBM) and 71.57% for the 18 other regressors employed in the study. This success rate is determined by the frequency of instances where models performed better when augmented data was used compared to instances with no augmentation. Access to the comparative code can be found in GitHub.</p><h2>Other Information</h2> <p> Published in: Expert Systems with Applications<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.eswa.2024.124118" target="_blank">https://dx.doi.org/10.1016/j.eswa.2024.124118</a></p>2024-05-20T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.eswa.2024.124118https://figshare.com/articles/journal_contribution/Oversampling_techniques_for_imbalanced_data_in_regression/26404000CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/264040002024-05-20T15:00:00Z |
| spellingShingle | Oversampling techniques for imbalanced data in regression Samir Brahim Belhaouari (9427347) Information and computing sciences Data management and data science Machine learning Data augmentation Machine learning AutoInflaters Nearest neighbor Imbalanced data |
| status_str | publishedVersion |
| title | Oversampling techniques for imbalanced data in regression |
| title_full | Oversampling techniques for imbalanced data in regression |
| title_fullStr | Oversampling techniques for imbalanced data in regression |
| title_full_unstemmed | Oversampling techniques for imbalanced data in regression |
| title_short | Oversampling techniques for imbalanced data in regression |
| title_sort | Oversampling techniques for imbalanced data in regression |
| topic | Information and computing sciences Data management and data science Machine learning Data augmentation Machine learning AutoInflaters Nearest neighbor Imbalanced data |