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Updated: Mar 19, 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

Composite large margin classifiers with latent subclasses for heterogeneous biomedical data.

Guanhua Chen¹, Yufeng Liu², Dinggang Shen³

¹Assistant Professor, Department of Biostatistics, Vanderbilt University, Nashville, TN 37203.

Statistical Analysis and Data Mining

|June 22, 2016

Summary

This summary is machine-generated.

This study introduces the Composite Large Margin Classifier (CLM) for high-dimensional data with latent subgroups. CLM offers accurate predictions like nonlinear methods while retaining the interpretability of linear classifiers.

Keywords:

Classification Large margin Latent subclasses Principal component analysis

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Area of Science:

Machine Learning
Biostatistics
Data Science

Background:

High-dimensional classification presents challenges in choosing between interpretable linear models and flexible nonlinear models.
Linear classifiers offer interpretability but struggle with complex data structures.
Nonlinear classifiers are flexible but can lack interpretability and overfit.

Purpose of the Study:

To propose a novel classification method, the Composite Large Margin Classifier (CLM), for data with potential latent subgroups.
To develop a classifier that balances prediction accuracy with interpretability.

Main Methods:

The Composite Large Margin Classifier (CLM) simultaneously finds three linear functions.
One function splits data, with two distinct linear classifiers handling each part.
Evaluated using Monte Carlo experiments and applied to Alzheimer's disease data.

Main Results:

CLM achieves prediction accuracy comparable to general nonlinear classifiers.
The method maintains the interpretability of traditional linear classifiers.
In Alzheimer's data, CLM reduced classification error and identified control subgroups at higher future risk.

Conclusions:

The Composite Large Margin Classifier (CLM) effectively addresses classification with latent subclasses in high-dimensional data.
CLM provides a valuable tool for scientific applications requiring both accuracy and interpretability.
The method shows promise in identifying at-risk subgroups for diseases like Alzheimer's.