A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems
Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented. This study addresses the classification problems with multi-class imbalanced datasets, which are inherently more complicated than binary imbala...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10829925/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841542545408000000 |
|---|---|
| author | Jiao Wang Norhashidah Awang |
| author_facet | Jiao Wang Norhashidah Awang |
| author_sort | Jiao Wang |
| collection | DOAJ |
| description | Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented. This study addresses the classification problems with multi-class imbalanced datasets, which are inherently more complicated than binary imbalanced problems. To tackle this problem, a novel and effective method called the One-vs-One Center Hybrid Synthetic Minority Over-sampling Technique (OCH-SMOTE) algorithm is proposed, which combines the enhanced Synthetic Minority Oversampling Techniques (SMOTE) with the One-vs-One (OVO) decomposition strategy. The OCH-SMOTE algorithm comprises two key components: the OVO strategy is used to decompose the multi-class imbalanced datasets, and the enhanced CH-SMOTE algorithm is used to generate the balanced training datasets to improve the classification performance. The OCH-SMOTE algorithm is extensively evaluated on 18 real-world multi-class imbalanced datasets, using the CART decision tree as the base classifier. The proposed method is compared with classical and state-of-the-art oversampling methods. On average, the OCH-SMOTE algorithm improves <inline-formula> <tex-math notation="LaTeX">$P_{macro}$ </tex-math></inline-formula>by 8.19%, MAVA by 9.19%, MG by 30.68%, MFM by 8.78%, and Kappa coefficient by 0.0462 across all datasets compared to the baseline methods. The experimental results demonstrate that the OCH-SMOTE algorithm significantly enhances multi-class imbalanced datasets classification performance. |
| format | Article |
| id | doaj-art-510c502a0b6240dba710f985ab59e1d4 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-510c502a0b6240dba710f985ab59e1d42025-01-14T00:02:32ZengIEEEIEEE Access2169-35362025-01-01136054606610.1109/ACCESS.2025.352667310829925A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance ProblemsJiao Wang0https://orcid.org/0000-0001-6022-1807Norhashidah Awang1https://orcid.org/0000-0002-2280-7193School of Mathematical Sciences, Universiti Sains Malaysia, Penang, MalaysiaSchool of Mathematical Sciences, Universiti Sains Malaysia, Penang, MalaysiaMulti-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented. This study addresses the classification problems with multi-class imbalanced datasets, which are inherently more complicated than binary imbalanced problems. To tackle this problem, a novel and effective method called the One-vs-One Center Hybrid Synthetic Minority Over-sampling Technique (OCH-SMOTE) algorithm is proposed, which combines the enhanced Synthetic Minority Oversampling Techniques (SMOTE) with the One-vs-One (OVO) decomposition strategy. The OCH-SMOTE algorithm comprises two key components: the OVO strategy is used to decompose the multi-class imbalanced datasets, and the enhanced CH-SMOTE algorithm is used to generate the balanced training datasets to improve the classification performance. The OCH-SMOTE algorithm is extensively evaluated on 18 real-world multi-class imbalanced datasets, using the CART decision tree as the base classifier. The proposed method is compared with classical and state-of-the-art oversampling methods. On average, the OCH-SMOTE algorithm improves <inline-formula> <tex-math notation="LaTeX">$P_{macro}$ </tex-math></inline-formula>by 8.19%, MAVA by 9.19%, MG by 30.68%, MFM by 8.78%, and Kappa coefficient by 0.0462 across all datasets compared to the baseline methods. The experimental results demonstrate that the OCH-SMOTE algorithm significantly enhances multi-class imbalanced datasets classification performance.https://ieeexplore.ieee.org/document/10829925/Multi-class imbalancedover-samplingSMOTE algorithmone-vs-one approach |
| spellingShingle | Jiao Wang Norhashidah Awang A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems IEEE Access Multi-class imbalanced over-sampling SMOTE algorithm one-vs-one approach |
| title | A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems |
| title_full | A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems |
| title_fullStr | A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems |
| title_full_unstemmed | A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems |
| title_short | A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems |
| title_sort | novel synthetic minority oversampling technique for multiclass imbalance problems |
| topic | Multi-class imbalanced over-sampling SMOTE algorithm one-vs-one approach |
| url | https://ieeexplore.ieee.org/document/10829925/ |
| work_keys_str_mv | AT jiaowang anovelsyntheticminorityoversamplingtechniqueformulticlassimbalanceproblems AT norhashidahawang anovelsyntheticminorityoversamplingtechniqueformulticlassimbalanceproblems AT jiaowang novelsyntheticminorityoversamplingtechniqueformulticlassimbalanceproblems AT norhashidahawang novelsyntheticminorityoversamplingtechniqueformulticlassimbalanceproblems |