Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification
Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretab...
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Published in | Mathematics (Basel) Vol. 9; no. 9; p. 936 |
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Abstract | Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection. |
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AbstractList | Imbalanced data exist in many classification problems. The classification of imbalanced data has remarkable challenges in machine learning. The support vector machine (SVM) and its variants are popularly used in machine learning among different classifiers thanks to their flexibility and interpretability. However, the performance of SVMs is impacted when the data are imbalanced, which is a typical data structure in the multi-category classification problem. In this paper, we employ the data-adaptive SVM with scaled kernel functions to classify instances for a multi-class population. We propose a multi-class data-dependent kernel function for the SVM by considering class imbalance and the spatial association among instances so that the classification accuracy is enhanced. Simulation studies demonstrate the superb performance of the proposed method, and a real multi-class prostate cancer image dataset is employed as an illustration. Not only does the proposed method outperform the competitor methods in terms of the commonly used accuracy measures such as the F-score and G-means, but also successfully detects more than 60% of instances from the rare class in the real data, while the competitors can only detect less than 20% of the rare class instances. The proposed method will benefit other scientific research fields, such as multiple region boundary detection. |
Author | Shao, Jianli Liu, Xin He, Wenqing |
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CitedBy_id | crossref_primary_10_1016_j_engappai_2023_106832 crossref_primary_10_3390_math9172068 crossref_primary_10_3390_math9232997 crossref_primary_10_1007_s11042_023_15725_y crossref_primary_10_3390_math9141680 crossref_primary_10_3390_app13064055 crossref_primary_10_1016_j_ecolind_2024_112117 crossref_primary_10_3390_math10142535 |
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SubjectTerms | Artificial intelligence Bias Classification Data mining Data structures data-adaptive kernel functions Food science image data Kernel functions Machine learning Medical research multi-category classifier predictive models support vector machine Support vector machines |
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Title | Kernel Based Data-Adaptive Support Vector Machines for Multi-Class Classification |
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