Kernel based online learning for imbalance multiclass classification

• المؤلفون الرئيسيون: Ding, Shuya, Mirza, Bilal, Lin, Zhiping, Cao, Jiuwen, Lai, Xiaoping, Nguyen, Tam Van, Sepulveda, Jose
• مؤلفون آخرون: School of Electrical and Electronic Engineering
• التنسيق: مقال
• اللغة: English
• منشور في: 2020
• الموضوعات: Engineering::Computer science and engineering; Class Imbalance; Extreme Learning Machine (ELM)
• الوصول للمادة أونلاين: https://hdl.handle.net/10356/138810

In this paper, we propose a weighted online sequential extreme learning machine with kernels (WOS-ELMK) for class imbalance learning (CIL). The existing online sequential extreme learning machine (OS-ELM) methods for CIL use random feature mapping. WOS-ELMK is the first OS-ELM method which uses kern...