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

DOI10.1080/03610926.2015.1048889zbMath1360.62352OpenAlexW2147061150MaRDI QIDQ2979033

Andreas Artemiou, Luke Smallman

Publication date: 2 May 2017

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: http://orca.cf.ac.uk/72823/1/revision_CiS-TaM.pdf

zbMATH Keywords

sufficient dimension reductioninverse regressionSMOTEzPSVM

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Graphical methods in statistics (62A09)

Related Items (1)

Evaluation of SMEs' credit decision based on support vector machine-logistics regression

Uses Software

• UCI-ml

Cites Work

• Principal support vector machines for linear and nonlinear sufficient dimension reduction
• Successive direction extraction for estimating the central subspace in a multiple-index regres\-sion
• Kernel dimension reduction in regression
• Contour regression: a general approach to dimension reduction
• A Cost Based Reweighted Scheme of Principal Support Vector Machine
• On Directional Regression for Dimension Reduction
• Two Modifications of CNN

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