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Related Experiment Video

Updated: Jun 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Probabilistic classifiers with high-dimensional data.

Kyung In Kim¹, Richard Simon

¹Biometric Research Branch, National Cancer Institute, 9000 Rockville Pike, MSC 7434, Bethesda, MD 20892-7434, USA.

Biostatistics (Oxford, England)

|November 20, 2010

Summary

This summary is machine-generated.

This study introduces new criteria for assessing probabilistic classifiers in medical classification, emphasizing the importance of well-calibrated predictions for high-dimensional data. Proper calibration and refinement are crucial for reliable medical decision-making.

Related Experiment Videos

Last Updated: Jun 6, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Biostatistics
Machine Learning
Medical Informatics

Background:

Probabilistic classifiers are vital for medical decision-making, especially in small n large p problems.
Existing probabilistic classifiers often lack sufficient attention and rigorous assessment methods for high-dimensional data.

Purpose of the Study:

To introduce and evaluate criteria for assessing probabilistic classifiers: well-calibratedness and refinement.
To develop and apply novel evaluation measures for probabilistic classification performance.
To assess existing and novel high-dimensional probabilistic classifiers.

Main Methods:

Development of two criteria: well-calibratedness and refinement.
Creation of corresponding evaluation measures.
Evaluation of published high-dimensional probabilistic classifiers and two extensions of the Bayesian compound covariate classifier.
Utilizing simulation studies and gene expression microarray data analysis.
Introduction of a cross-validation method for evaluating calibration and refinement.

Main Results:

Proper probabilistic classification is more challenging than deterministic classification in high-dimensional settings.
Ensuring probabilistic classifiers are well-calibrated or not 'anticonservative' is critical.
The study provides evaluations for several probabilistic classifiers regarding calibration and refinement.
Refinement performance varies with sample size under different signal conditions.

Conclusions:

New methods for evaluating probabilistic classifiers are essential for reliable medical classification.
The developed criteria and measures aid in selecting appropriate probabilistic models for high-dimensional medical data.
The cross-validation method offers a versatile approach for assessing any probabilistic classifier's performance.