L1-penalized AUC-optimization with a surrogate loss

Hyungwoo Kim, Seung Jun Shin

Research output: Contribution to journalArticlepeer-review


The area under the ROC curve (AUC) is one of the most common criteria used to measure the overall performance of binary classifiers for a wide range of machine learning problems. In this article, we propose a L1-penalized AUC-optimization classifier that directly maximizes the AUC for high-dimensional data. Toward this, we employ the AUC-consistent surrogate loss function and combine the L1-norm penalty which enables us to estimate coefficients and select informative variables simultaneously. In addition, we develop an efficient optimization algorithm by adopting k-means clustering and proximal gradient descent which enjoys computational advantages to obtain solutions for the proposed method. Numerical simulation studies demonstrate that the proposed method shows promising performance in terms of prediction accuracy, variable selectivity, and computational costs.

Original languageEnglish
Pages (from-to)203-212
Number of pages10
JournalCommunications for Statistical Applications and Methods
Issue number2
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2024 The Korean Statistical Society, and Korean International Statistical Society. All Rights Reserved.


  • AUC consistency
  • AUC-optimization
  • clustering and proximal gradient descent
  • L-norm penalty
  • variable selection

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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