Support vector machine and optimal parameter selection for high-dimensional imbalanced data

• Published in: Communications in statistics. Simulation and computation Vol. 51; no. 11; pp. 6739 - 6754
• Main Author: Nakayama, Yugo
• Format: Journal Article
• Language: English
• Published: Philadelphia Taylor & Francis 02.11.2022; Taylor & Francis Ltd
• Subjects: Asymptotic properties; Classification; HDLSS; Imbalanced data; Kernel functions; Optimization; Parameters; Primary 62H30; Regularization; Robustness (mathematics); Secondary 62G20; Support vector machine; Support vector machines

In this article, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we treat high-dimensional imbalanced data. We investigate behaviors of SVM for a regularization parameter C in a framework of kernel functions. We sh...