Jump to ContentJump to Main Navigation
Show Summary Details

The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

2 Issues per year

IMPACT FACTOR 2015: 0.667
5-year IMPACT FACTOR: 1.188

SCImago Journal Rank (SJR) 2015: 0.495
Source Normalized Impact per Paper (SNIP) 2015: 0.180
Impact per Publication (IPP) 2015: 0.319

Mathematical Citation Quotient (MCQ) 2015: 0.04

See all formats and pricing

HingeBoost: ROC-Based Boost for Classification and Variable Selection

Zhu Wang
  • Connecticut Children’s Medical Center and University of Connecticut School of Medicine
Published Online: 2011-02-04 | DOI: https://doi.org/10.2202/1557-4679.1304

In disease classification, a traditional technique is the receiver operative characteristic (ROC) curve and the area under the curve (AUC). With high-dimensional data, the ROC techniques are needed to conduct classification and variable selection. The current ROC methods do not explicitly incorporate unequal misclassification costs or do not have a theoretical grounding for optimizing the AUC. Empirical studies in the literature have demonstrated that optimizing the hinge loss can maximize the AUC approximately. In theory, minimizing the hinge rank loss is equivalent to minimizing the AUC in the asymptotic limit. In this article, we propose a novel nonparametric method HingeBoost to optimize a weighted hinge loss incorporating misclassification costs. HingeBoost can be used to construct linear and nonlinear classifiers. The estimation and variable selection for the hinge loss are addressed by a new boosting algorithm. Furthermore, the proposed twin HingeBoost can select more sparse predictors. Some properties of HingeBoost are studied as well. To compare HingeBoost with existing classification methods, we present empirical study results using data from simulations and a prostate cancer study with mass spectrometry-based proteomics.

Keywords: functional gradient descent; support vector machine; ROC; classification; misclassification costs

About the article

Published Online: 2011-02-04

Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.2202/1557-4679.1304. Export Citation

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

Tim Appelhans, Ephraim Mwangomo, Insa Otte, Florian Detsch, Thomas Nauss, and Andreas Hemp
International Journal of Climatology, 2015, Page n/a
P. Bühlmann, J. Gertheiss, S. Hieke, T. Kneib, S. Ma, M. Schumacher, G. Tutz, C.-Y. Wang, Z. Wang, and A. Ziegler
Methods of Information in Medicine, 2014, Volume 53, Number 6, Page 436
A. Mayr, H. Binder, O. Gefeller, and M. Schmid
Methods of Information in Medicine, 2014, Volume 53, Number 6, Page 428
Feihan Lu and Eva Petkova
Statistics in Medicine, 2014, Volume 33, Number 3, Page 401

Comments (0)

Please log in or register to comment.
Log in