HingeBoost: ROC-Based Boost for Classification and Variable Selection : The International Journal of Biostatistics

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The International Journal of Biostatistics

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

IMPACT FACTOR 2014: 0.741
5-year IMPACT FACTOR: 1.475

SCImago Journal Rank (SJR) 2014: 1.247
Source Normalized Impact per Paper (SNIP) 2014: 1.078
Impact per Publication (IPP) 2014: 1.206

Mathematical Citation Quotient (MCQ) 2014: 0.07

HingeBoost: ROC-Based Boost for Classification and Variable Selection

Zhu Wang1

1Connecticut Children’s Medical Center and University of Connecticut School of Medicine

Citation Information: The International Journal of Biostatistics. Volume 7, Issue 1, Pages 1–30, ISSN (Online) 1557-4679, DOI: 10.2202/1557-4679.1304, February 2011

Publication History

Published Online:

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

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