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Related Experiment Video

Updated: May 19, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Efficient optimization of performance measures by classifier adaptation.

Nan Li1, Ivor W Tsang, Zhi-Hua Zhou

  • 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China, and the School of Mathematical Sciences, Soochow University, Suzhou 215006, China. lin@lamda.nju.edu.cn

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 8, 2012
PubMed
Summary

This study introduces CAPO, a novel two-step method for training machine learning classifiers. CAPO efficiently adapts auxiliary classifiers to optimize specific performance measures, including nonlinear and nonsmooth ones.

Related Experiment Videos

Last Updated: May 19, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Computational Science

Background:

  • Traditional machine learning focuses on isolated classifier training.
  • Learning nonlinear classifiers for nonlinear and nonsmooth performance measures remains challenging.

Purpose of the Study:

  • To propose a novel two-step approach, CAPO, for training machine learning classifiers that optimize domain-specific performance measures.
  • To address the difficulty of directly learning nonlinear classifiers for nonlinear and nonsmooth performance measures.

Main Methods:

  • CAPO employs a two-step process: first, training nonlinear auxiliary classifiers using existing methods.
  • Second, adapting these auxiliary classifiers to specific performance measures by reducing the problem to a quadratic program.

Main Results:

  • CAPO generates nonlinear classifiers optimizing various performance measures, including contingency table-based metrics and AUC.
  • The method maintains high computational efficiency, outperforming even linear SVM(perf).

Conclusions:

  • CAPO offers an effective and computationally efficient solution for learning classifiers that optimize specific performance measures.
  • The approach successfully handles nonlinear and nonsmooth performance measures by leveraging nonlinear auxiliary classifiers.