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

• Main Authors: CAO, Bin, LIU, Yuqi, HOU, Chenyu, FAN, Jing, ZHENG, Baihua, JIN, Jianwei
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Support vector machines; Tuning; Optimization; Kernel; Training; Standards; Fans; Databases and Information Systems; Theory and Algorithms
• Online Access: 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
• Institution: Singapore Management University

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...