Expediting the accuracy-improving process of SVMs for class imbalance learning

Expediting the accuracy-improving process of SVMs for class imbalance learning

To improve the classification performance of support vector machines (SVMs) on imbalanced datasets, cost-sensitive learning methods have been proposed, e.g., DEC (Different Error Costs) and FSVM-CIL (Fuzzy SVM for Class Imbalance Learning). They relocate the hyperplane by adjusting the costs associa...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	CAO, Bin, LIU, Yuqi, HOU, Chenyu, FAN, Jing, ZHENG, Baihua, JIN, Jianwei
التنسيق:	text
اللغة:	English
منشور في:	Institutional Knowledge at Singapore Management University 2021
الموضوعات:	Support vector machines Tuning Optimization Kernel Training Standards Fans Databases and Information Systems Theory and Algorithms
الوصول للمادة أونلاين:	https://ink.library.smu.edu.sg/sis_research/5097 https://ink.library.smu.edu.sg/context/sis_research/article/6100/viewcontent/15._Expediting_the_Accuracy_improving_Process_of_XVMS_of_Class_Imbalance_Learning_TKDEFeb2020.pdf
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