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Updated: Feb 26, 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

A genetic algorithm-based framework for online sparse feature selection in data streams.

Guanyu Liu^1,2, Jinhang Liu¹, Guifan He¹

¹College of Computer and Information Science, Southwest University, Chongqing, China.

Frontiers in Big Data

|February 25, 2026

Summary

This summary is machine-generated.

A new method, genetic algorithm-based online sparse streaming feature selection (GA-OS²FS), improves high-dimensional data analysis by imputing missing values and evaluating features effectively, leading to higher accuracy.

Keywords:

feature selection genetic algorithm latent factor analysis missing data online learning

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Area of Science:

Machine Learning
Data Mining
Big Data Analytics

Background:

Online streaming feature selection (OSFS) is crucial for high-dimensional data streams.
Incomplete data presents a significant challenge for existing OSFS and OS²FS methods.
Current OS²FS methods struggle with feature evaluation, impacting performance.

Purpose of the Study:

To introduce a novel genetic algorithm-based online sparse streaming feature selection (GA-OS²FS) method.
To address limitations in feature evaluation within existing OS²FS approaches.
To enhance the accuracy of feature selection in data streams with missing values.

Main Methods:

Imputation of missing values using a latent factor analysis model.
Application of a genetic algorithm for feature importance assessment.
Development of GA-OS²FS for online sparse streaming feature selection.

Main Results:

GA-OS²FS demonstrates superior performance compared to state-of-the-art OSFS and OS²FS methods.
The proposed method consistently achieves higher accuracy across six real-world datasets.
Optimal feature subsets are selected, leading to improved analytical outcomes.

Conclusions:

GA-OS²FS effectively handles missing data in high-dimensional streams.
The integration of genetic algorithms enhances feature evaluation in streaming data.
The novel GA-OS²FS method offers a significant advancement in online feature selection.