Expediting the Accuracy-Improving Process of SVMs for Class Imbalance Learning
IEEE Transactions on Knowledge and Data Engineering(2021)
摘要
To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., Different Error Costs (DEC) and Fuzzy SVM for Class Imbalance Learning (FSVM-CIL). They relocate the hyperplane by adjusting the costs associated with misclassifying samples. However, the error costs are determined either empirically or by pe...
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Support vector machines,Tuning,Optimization,Kernel,Training,Standards,Fans
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