IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification
Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are th...
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| Format: | Article |
| Language: | English |
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Ital Publication
2023-10-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/1758 |
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| author | Lilis Yuningsih Gede Angga Pradipta Dadang Hermawan Putu Desiana Wulaning Ayu Dandy Pramana Hostiadi Roy Rudolf Huizen |
| author_facet | Lilis Yuningsih Gede Angga Pradipta Dadang Hermawan Putu Desiana Wulaning Ayu Dandy Pramana Hostiadi Roy Rudolf Huizen |
| author_sort | Lilis Yuningsih |
| collection | DOAJ |
| description | Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods.
Doi: 10.28991/ESJ-2023-07-05-04
Full Text: PDF |
| first_indexed | 2024-03-08T14:25:23Z |
| format | Article |
| id | doaj.art-9cf1f44b02d14c4eb4695ea8c264660e |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-03-08T14:25:23Z |
| publishDate | 2023-10-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-9cf1f44b02d14c4eb4695ea8c264660e2024-01-13T07:27:37ZengItal PublicationEmerging Science Journal2610-91822023-10-01751501151610.28991/ESJ-2023-07-05-04540IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set ClassificationLilis Yuningsih0Gede Angga Pradipta1Dadang Hermawan2Putu Desiana Wulaning Ayu3Dandy Pramana Hostiadi4Roy Rudolf Huizen5Department of Information System, Faculty Computer and Informatics, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar 80234,Post Graduate Department of Information System, Faculty Computer and Informatics, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar 80234,Department of Digital Bussines, Faculty Bussines and Vocation, Institut Teknologi dan Bisnis STIKOM Bali Denpasar 80234,Post Graduate Department of Information System, Faculty Computer and Informatics, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar 80234,Post Graduate Department of Information System, Faculty Computer and Informatics, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar 80234,Post Graduate Department of Information System, Faculty Computer and Informatics, Institut Teknologi dan Bisnis STIKOM Bali, Denpasar 80234,Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are the phenomenon of noise, small disjunct samples, and overfitting due to a high imbalance ratio in a dataset. A high level of imbalance ratio and low variance conditions cause the results of synthetic data generation to be collected in narrow areas and conflicting regions among classes and make them susceptible to overfitting during the learning process by machine learning methods. Therefore, this research proposes a combination between Radius-SMOTE and Bagging Algorithm called the IRS-BAG Model. For each sub-sample generated by bootstrapping, oversampling was done using Radius SMOTE. Oversampling on the sub-sample was likely to overcome overfitting problems that might occur. Experiments were carried out by comparing the performance of the IRS-BAG model with various previous oversampling methods using the imbalanced public dataset. The experiment results using three different classifiers proved that all classifiers had gained a notable improvement when combined with the proposed IRS-BAG model compared with the previous state-of-the-art oversampling methods. Doi: 10.28991/ESJ-2023-07-05-04 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/1758imbalanced dataoversamplingsmotebaggingclassificationmachine learning. |
| spellingShingle | Lilis Yuningsih Gede Angga Pradipta Dadang Hermawan Putu Desiana Wulaning Ayu Dandy Pramana Hostiadi Roy Rudolf Huizen IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification Emerging Science Journal imbalanced data oversampling smote bagging classification machine learning. |
| title | IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification |
| title_full | IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification |
| title_fullStr | IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification |
| title_full_unstemmed | IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification |
| title_short | IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification |
| title_sort | irs bag integrated radius smote algorithm with bagging ensemble learning model for imbalanced data set classification |
| topic | imbalanced data oversampling smote bagging classification machine learning. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/1758 |
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