Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning
Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is an alternative kind of methods to deal with imbalanced lear...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10820828/ |
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| author | Haiou Qin |
| author_facet | Haiou Qin |
| author_sort | Haiou Qin |
| collection | DOAJ |
| description | Learning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is an alternative kind of methods to deal with imbalanced learning. In this paper, we propose a new undersampling method based on maximal information coefficient (including two algorithms MICU-1 and MICU-2) to rebalance the datasets. In order to evaluate the effectiveness of the method, 20 highly- imbalanced datasets are used for the benchmarks. Results show that compared with other undersampling methods, maximal information coefficient-based undersampling method are competitive in terms of G-mean and F-measure. |
| format | Article |
| id | doaj-art-1bcf831d9fce498c89082d5d5a0003a3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-1bcf831d9fce498c89082d5d5a0003a32025-01-10T00:01:23ZengIEEEIEEE Access2169-35362025-01-01134126413510.1109/ACCESS.2025.352547510820828Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced LearningHaiou Qin0https://orcid.org/0009-0006-6773-9215School of Information Engineering, Nanchang Institute of Technology, Nanchang, ChinaLearning from highly-imbalanced datasets is still a big challenge in the field of machine learning because models created by general learning algorithms are weak in recognizing the samples from the minority class correctly. Undersampling is an alternative kind of methods to deal with imbalanced learning. In this paper, we propose a new undersampling method based on maximal information coefficient (including two algorithms MICU-1 and MICU-2) to rebalance the datasets. In order to evaluate the effectiveness of the method, 20 highly- imbalanced datasets are used for the benchmarks. Results show that compared with other undersampling methods, maximal information coefficient-based undersampling method are competitive in terms of G-mean and F-measure.https://ieeexplore.ieee.org/document/10820828/Imbalanced classificationimbalanced learningmaximal information coefficientmaximal information coefficient-based undersamplingundersampling |
| spellingShingle | Haiou Qin Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning IEEE Access Imbalanced classification imbalanced learning maximal information coefficient maximal information coefficient-based undersampling undersampling |
| title | Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning |
| title_full | Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning |
| title_fullStr | Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning |
| title_full_unstemmed | Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning |
| title_short | Maximal Information Coefficient-Based Undersampling Method for Highly-Imbalanced Learning |
| title_sort | maximal information coefficient based undersampling method for highly imbalanced learning |
| topic | Imbalanced classification imbalanced learning maximal information coefficient maximal information coefficient-based undersampling undersampling |
| url | https://ieeexplore.ieee.org/document/10820828/ |
| work_keys_str_mv | AT haiouqin maximalinformationcoefficientbasedundersamplingmethodforhighlyimbalancedlearning |