A study on imbalance support vector machine algorithms for sufficient dimension reduction

Smallman, Luke ORCID: https://orcid.org/0000-0002-4114-310X and Artemiou, Andreas ORCID: https://orcid.org/0000-0002-7501-4090 2017. A study on imbalance support vector machine algorithms for sufficient dimension reduction. Communications in Statistics - Theory and Methods 46 (6) , pp. 2751-2763. 10.1080/03610926.2015.1048889

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Abstract

Li, Artemiou and Li (2011) presented the novel idea of using Support Vector Machines to perform sufficient dimension reduction. In this work, we investigate the potential improvement in recovering the dimension reduction subspace when one changes the Support Vector Machines algorithm to treat imbalance based on several proposals in the machine learning literature. We find out that in most situations, treating the imbalance nature of the slices will help improve the estimation. Our results are verified through simulation and real data applications

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Mathematics
Subjects:	Q Science > QA Mathematics
Uncontrolled Keywords:	Inverse regression, SMOTE, sufficient dimension reduction, zPSVM MATHEMATICS SUBJECT CLASSIFICATION: 62H30, 62-09, 68T10, 62G08
Additional Information:	Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/0361-0926/ (accessed 22/06/2016)
Publisher:	Taylor & Francis
ISSN:	0361-0926
Funders:	London Mathematical Society
Date of First Compliant Deposit:	30 March 2016
Date of Acceptance:	30 April 2015
Last Modified:	18 Jan 2025 23:00
URI:	https://orca.cardiff.ac.uk/id/eprint/72823

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