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Related Experiment Video

Updated: Jun 1, 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

Ultrahigh dimensional feature selection: beyond the linear model.

Jianqing Fan1, Richard Samworth, Yichao Wu

  • 1Department of Operations Research and Financial Engineering, Princeton University, Princeton, NJ 08540 USA.

Journal of Machine Learning Research : JMLR
|May 24, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced iterative sure independence screening (ISIS) method for variable selection in high-dimensional data. The new approach improves feature selection accuracy, especially in classification tasks, by reducing false positives.

Related Experiment Videos

Last Updated: Jun 1, 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:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Variable selection is crucial in high-dimensional data analysis for scientific discovery and decision-making.
  • Existing methods like correlation ranking and two-sample t-tests have limitations in certain scenarios, particularly when features are marginally unrelated but jointly related to the response.
  • Iteratively Sure Independent Screening (ISIS) was developed to address some of these limitations.

Purpose of the Study:

  • To extend the Iteratively Sure Independent Screening (ISIS) method to a general pseudo-likelihood framework, encompassing generalized linear models.
  • To improve upon existing feature screening techniques by allowing feature deletion in the iterative process and reducing the false selection rate.
  • To enable effective feature selection in high-dimensional classification problems where traditional methods like the two-sample t-test may fail.

Main Methods:

  • The study extends ISIS to a general pseudo-likelihood framework without explicit residual definition.
  • The proposed method incorporates feature deletion within the iterative selection process, enhancing efficiency.
  • A novel technique is introduced to mitigate the false selection rate during the feature screening phase.

Main Results:

  • The generalized ISIS method demonstrates improved performance in variable selection compared to existing techniques, particularly in high-dimensional classification.
  • The new approach successfully identifies important features even when they are marginally unrelated but jointly influence the response variable.
  • Simulations and real-world data analyses confirm the methodology's effectiveness in reducing false selections and improving feature screening.

Conclusions:

  • The extended ISIS framework offers a robust and flexible approach to variable selection in high-dimensional settings.
  • This methodology provides a valuable tool for scientific discovery and decision-making, especially in complex classification tasks.
  • The technique effectively addresses limitations of prior methods, enhancing the reliability of feature selection in modern data analysis.