A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data
Early breast cancer diagnosis is crucial for improving treatment outcomes for women. Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. To t...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10794777/ |
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| author | Zhenzhen Wang Junde Xie Jia Zhang |
| author_facet | Zhenzhen Wang Junde Xie Jia Zhang |
| author_sort | Zhenzhen Wang |
| collection | DOAJ |
| description | Early breast cancer diagnosis is crucial for improving treatment outcomes for women. Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. To tackle this issue, we propose a robust enhanced ensemble learning method (REEL). Specifically, a double-level over-sampling technology is developed to increase the diversity of synthesized minority breast cancer samples before model training, and an improved Random Forest is proposed to reconcile the bias and variance. In addition, a data-driven based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers. Experimental results on breast cancer datasets and 19 other imbalanced datasets validate that our method outperforms other algorithms in terms of accuracy, F1 score, and AUC.These findings confirm that our method can further improve classification accuracy and has significant application value in the diagnosis of breast cancer. |
| format | Article |
| id | doaj-art-e886a9adebac4cea8d45576bc122f844 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e886a9adebac4cea8d45576bc122f8442024-12-20T00:00:43ZengIEEEIEEE Access2169-35362024-01-011218977618978810.1109/ACCESS.2024.351637610794777A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced DataZhenzhen Wang0https://orcid.org/0009-0006-6930-2834Junde Xie1https://orcid.org/0009-0008-8697-7272Jia Zhang2https://orcid.org/0000-0001-5740-715XSchool of Chemistry and Chemical Engineering, Central South University of Forestry and Technology, Changsha, ChinaHunan Provincial Key Laboratory of Geochemical Processes and Resource Environmental Effects, Geophysical and Geochemical Survey Institute of Hunan, Changsha, ChinaSchool of Computer and Mathematics, Central South University of Forestry and Technology, Changsha, ChinaEarly breast cancer diagnosis is crucial for improving treatment outcomes for women. Addressing class imbalance in breast cancer data is essential for enhancing detection accuracy, yet traditional machine learning methods often overlook this imbalance, limiting their classification performance. To tackle this issue, we propose a robust enhanced ensemble learning method (REEL). Specifically, a double-level over-sampling technology is developed to increase the diversity of synthesized minority breast cancer samples before model training, and an improved Random Forest is proposed to reconcile the bias and variance. In addition, a data-driven based particle swarm optimization algorithm automatically is used to select the value of parameters for base classifiers. Experimental results on breast cancer datasets and 19 other imbalanced datasets validate that our method outperforms other algorithms in terms of accuracy, F1 score, and AUC.These findings confirm that our method can further improve classification accuracy and has significant application value in the diagnosis of breast cancer.https://ieeexplore.ieee.org/document/10794777/Breast cancer diagnosisimbalanced data classificationdouble-layer oversamplingrandom forest |
| spellingShingle | Zhenzhen Wang Junde Xie Jia Zhang A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data IEEE Access Breast cancer diagnosis imbalanced data classification double-layer oversampling random forest |
| title | A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data |
| title_full | A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data |
| title_fullStr | A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data |
| title_full_unstemmed | A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data |
| title_short | A Robust Enhanced Ensemble Learning Method for Breast Cancer Data Diagnosis on Imbalanced Data |
| title_sort | robust enhanced ensemble learning method for breast cancer data diagnosis on imbalanced data |
| topic | Breast cancer diagnosis imbalanced data classification double-layer oversampling random forest |
| url | https://ieeexplore.ieee.org/document/10794777/ |
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