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Updated: May 9, 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

A feature selection method for multivariate performance measures.

Qi Mao1, Ivor Wai-Hung Tsang

  • 1School of Computer Engineering, Nanyang Technological University, Singapore. Qmao1@ntu.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence
|July 23, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel feature selection framework optimizing multivariate performance measures, outperforming existing methods for tasks like image retrieval and text classification. The approach enhances model accuracy, especially with limited feature subsets.

Related Experiment Videos

Last Updated: May 9, 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
  • Data Science
  • Computer Vision

Background:

  • Traditional feature selection methods primarily focus on classification error, limiting their effectiveness for applications requiring optimization of specific multivariate performance measures.
  • Existing techniques often struggle with high-dimensional data and diverse loss functions, necessitating advanced approaches for broader applicability.

Purpose of the Study:

  • To propose a generalized sparse regularizer and a unified feature selection framework capable of optimizing general loss functions, particularly multivariate performance measures.
  • To develop an efficient algorithm for solving the challenging high-dimensional optimization problem presented by the novel feature selection paradigm.
  • To extend the proposed method for multiple-instance learning problems, addressing specific needs in that domain.

Main Methods:

  • A generalized sparse regularizer was developed to unify feature selection for various loss functions.
  • A two-layer cutting plane algorithm was designed and analyzed for convergence to efficiently solve the high-dimensional optimization problem.
  • The framework was adapted for multiple-instance learning scenarios, enhancing its versatility.

Main Results:

  • The proposed unified feature selection framework demonstrates superior performance compared to state-of-the-art methods.
  • Extensive experiments on large-scale, high-dimensional datasets show the method outperforms l₁-SVM and SVM-RFE for small feature subsets.
  • The approach achieves significantly improved F₁-scores compared to SVM(perf), highlighting its effectiveness in optimizing multivariate measures.

Conclusions:

  • The proposed generalized sparse regularizer and unified framework offer a powerful new approach to feature selection, especially for optimizing multivariate performance measures.
  • The developed two-layer cutting plane algorithm provides an efficient solution for high-dimensional data, making the method practical for real-world applications.
  • The method's adaptability to multiple-instance learning and superior empirical performance validate its significance in advancing feature selection techniques.