Robust support vector machine for high-dimensional imbalanced data

In this paper, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we investigate the behavior of soft-margin SVM for the regularization parameter C. We show that SVM cannot handle imbalanced classification and SVM is...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 50; no. 5; pp. 1524 - 1540
Main Author Nakayama, Yugo
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 04.05.2021
Taylor & Francis Ltd
Subjects
Asymptotic properties
HDLSS
imbalanced data
large p small n
Primary 62H30
Regularization
Robustness
Secondary 62G20
Support vector machines
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Summary:In this paper, we consider asymptotic properties of support vector machine (SVM) in high-dimension, low-sample-size (HDLSS) settings. In particular, we investigate the behavior of soft-margin SVM for the regularization parameter C. We show that SVM cannot handle imbalanced classification and SVM is very biased in HDLSS settings. In order to overcome such difficulties, we propose a robust SVM (RSVM). We show that RSVM gives preferable performances in HDLSS settings. Finally, we check the performance of RSVM in actual data analyses.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2019.1586922