HingeBoost: ROC-based boost for classification and variable selection

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18 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number13
JournalInternational Journal of Biostatistics
Volume7
Issue number1
DOIs
StatePublished - 2011
Externally publishedYes

Keywords

  • ROC
  • classification
  • functional gradient descent
  • misclassification costs
  • support vector machine

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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