A study on imbalance support vector machine algorithms for sufficient dimension reduction. Issue 6 (19th March 2017)

• Record Type: Journal Article
• Authors: Smallman, Luke; Artemiou, Andreas
• Is Part Of: Communications in statistics. Volume 46:Issue 6(2017)
• Page Start: 2751
• Page End: 2763
• Publication Date: 2017-03-19
• Subjects: Inverse regression -- SMOTE -- sufficient dimension reduction -- zPSVM; 62H30 -- 62-09 -- 68T10 -- 62G08; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2015.1048889
• Languages: English
• ISSNs: 0361-0926

Abstract

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.