A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification
Imbalanced data is an important issues and challenges faced in data classification. This will lead to poor performance of binary classifiers, this is due to bias in classification in favour of the majority class and lack of understanding of the influence of the minority class, while the minority cla...
| Main Authors: | , |
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| Format: | Article |
| Language: | Arabic |
| Published: |
College of Education for Pure Sciences
2023-12-01
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| Series: | مجلة التربية والعلم |
| Subjects: | |
| Online Access: | https://edusj.mosuljournals.com/article_180971_2e8e268f4bdbc7d511ffb728d5b70f64.pdf |
| _version_ | 1797447425578762240 |
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| author | Shaymaa Ahmed Razoqi Ghayda Al-Talib |
| author_facet | Shaymaa Ahmed Razoqi Ghayda Al-Talib |
| author_sort | Shaymaa Ahmed Razoqi |
| collection | DOAJ |
| description | Imbalanced data is an important issues and challenges faced in data classification. This will lead to poor performance of binary classifiers, this is due to bias in classification in favour of the majority class and lack of understanding of the influence of the minority class, while the minority class is usually the most important in the classification process. In order to find a compromise between the information loss and balance the data set before applying the classification, the research proposed a hybrid algorithm based on the use of clustering methods to divide the majority class into subgroups in the first phase, and using a method to encode the majority class. The Algorithm used the code to group samples that are similar to each other and reduce the majority class count. At the same time, the Synthetic Minority Oversampling Technique (SMOTE) was used to increase the number of minority class samples in the next phase. The study examined the impact of the proposed algorithm on five classifiers based on the AUC and F-score post-classification performance parameters using benchmark datasets with different sizes and imbalance factors. The results showed that the proposed algorithm significantly improved the performance of the classifiers when applied to the resampled data. |
| first_indexed | 2024-03-09T13:55:45Z |
| format | Article |
| id | doaj.art-1b712fa763314e958bbab8f2dd1794cd |
| institution | Directory Open Access Journal |
| issn | 1812-125X 2664-2530 |
| language | Arabic |
| last_indexed | 2024-03-09T13:55:45Z |
| publishDate | 2023-12-01 |
| publisher | College of Education for Pure Sciences |
| record_format | Article |
| series | مجلة التربية والعلم |
| spelling | doaj.art-1b712fa763314e958bbab8f2dd1794cd2023-11-30T18:30:02ZaraCollege of Education for Pure Sciencesمجلة التربية والعلم1812-125X2664-25302023-12-01324819010.33899/edusj.2023.143612.1393180971A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data ClassificationShaymaa Ahmed Razoqi0Ghayda Al-Talib1Department of Computer Science, College of Education for Pure Science, University of Mosul, Mosul, IRAQDepartment of Computer Science, College of Computer Science and Mathematics, University of Mosul, Mosul, IRAQImbalanced data is an important issues and challenges faced in data classification. This will lead to poor performance of binary classifiers, this is due to bias in classification in favour of the majority class and lack of understanding of the influence of the minority class, while the minority class is usually the most important in the classification process. In order to find a compromise between the information loss and balance the data set before applying the classification, the research proposed a hybrid algorithm based on the use of clustering methods to divide the majority class into subgroups in the first phase, and using a method to encode the majority class. The Algorithm used the code to group samples that are similar to each other and reduce the majority class count. At the same time, the Synthetic Minority Oversampling Technique (SMOTE) was used to increase the number of minority class samples in the next phase. The study examined the impact of the proposed algorithm on five classifiers based on the AUC and F-score post-classification performance parameters using benchmark datasets with different sizes and imbalance factors. The results showed that the proposed algorithm significantly improved the performance of the classifiers when applied to the resampled data.https://edusj.mosuljournals.com/article_180971_2e8e268f4bdbc7d511ffb728d5b70f64.pdfbig data,,,،classification,,,،imbalanced problem,,,،resampling,,,،clustering |
| spellingShingle | Shaymaa Ahmed Razoqi Ghayda Al-Talib A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification مجلة التربية والعلم big data,, ,،classification,, ,،imbalanced problem,, ,،resampling,, ,،clustering |
| title | A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification |
| title_full | A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification |
| title_fullStr | A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification |
| title_full_unstemmed | A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification |
| title_short | A Hybrid Undersampling-SMOTE Method for Imbalanced Big Data Classification |
| title_sort | hybrid undersampling smote method for imbalanced big data classification |
| topic | big data,, ,،classification,, ,،imbalanced problem,, ,،resampling,, ,،clustering |
| url | https://edusj.mosuljournals.com/article_180971_2e8e268f4bdbc7d511ffb728d5b70f64.pdf |
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