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

Luke Smallman and Andreas Artemiou

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 6, 2751-2763

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

Date: 2017
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DOI: 10.1080/03610926.2015.1048889

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