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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 multiple filter-wrapper feature selection algorithm based on process optimization mechanism for high-dimensional

Yongtao Shi¹, Yuefeng Zheng¹, Xiaotong Bai¹

¹School of Mathematics and Computer, Jilin Normal University, Siping, Jilin, China.

|December 11, 2025

Summary

This summary is machine-generated.

A new hybrid feature selection algorithm improves high-dimensional data analysis by creating diverse feature subsets. It enhances classification accuracy and significantly reduces dimensionality, overcoming limitations of existing methods.

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Area of Science:

Machine Learning
Data Science
Bioinformatics

Background:

High-dimensional data presents challenges for traditional machine learning models.
Existing hybrid feature selection methods often produce homogeneous subsets, limiting their effectiveness.
Novel approaches are needed to enhance feature subset diversity and optimization performance.

Purpose of the Study:

To introduce a novel hybrid feature selection algorithm, the Hybrid Multiple Filter-Wrapper algorithm.
To address the homogeneity issue in feature subsets generated by current methods.
To improve classification accuracy and dimensionality reduction for high-dimensional datasets.

Main Methods:

A dual-module structure combining random forest for initial reduction and a novel bivariate filter (minimum Spearman-Maximum Mutual Information) for correlation/redundancy assessment.
Integration of swarm intelligence algorithms (Grey Wolf and Chaotic Dung Beetle) with chaos theory for enhanced exploration and exploitation.
A dynamic process optimization mechanism using random laser intensity fluctuations to prevent local optima, incorporating filter restarts and chaos-based population resets.

Main Results:

The proposed algorithm significantly outperforms ten other hybrid algorithms across ten benchmark datasets.
Achieved at least a 1.3% higher average classification accuracy.
Reduced feature subset length by at least 8 units and dimensionality to less than 0.45% of the original, with statistically significant improvements.

Conclusions:

The Hybrid Multiple Filter-Wrapper algorithm effectively generates diverse feature subsets for high-dimensional data.
The novel integration of filter, wrapper, swarm intelligence, and chaos theory provides superior performance.
This method offers a robust solution for improving classification accuracy and achieving significant dimensionality reduction.