Large scale kernel methods for online AUC maximization

• Main Authors: DING, Yi, LIU, Chenghao, ZHAO, Peilin, HOI, Steven C. H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2017
• Subjects: Kernel; Computational modeling; Approximation algorithms; Machine learning algorithms; Support vector machines; Learning systems; Computer Sciences; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/3969; https://ink.library.smu.edu.sg/context/sis_research/article/4971/viewcontent/6._Large_Scale_Kernel_Methods_for_Online_AUC__ICDM2017_.pdf
• Institution: Singapore Management University

Learning to optimize AUC performance for classifying label imbalanced data in online scenarios has been extensively studied in recent years. Most of the existing work has attempted to address the problem directly in the original feature space, which may not suitable for non-linearly separable datase...