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Related Experiment Video

Updated: Jul 4, 2025

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

S-shaped grey wolf optimizer-based FOX algorithm for feature selection.

Afi Kekeli Feda¹, Moyosore Adegboye², Oluwatayomi Rereloluwa Adegboye³

¹Management Information System Department, European University of Lefke, Mersin, 10, Turkey.

|January 31, 2024

Summary

The enhanced FOX-GWO algorithm improves feature selection by integrating Grey Wolf Optimizer, overcoming local optima in high-dimensional data. This boosts accuracy and reduces dimensionality effectively.

Keywords:

FOX algorithm Feature selection S-Shaped transfer function

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Area of Science:

Computational intelligence
Machine learning
Data science

Background:

The FOX algorithm, a metaheuristic, shows promise but struggles with local optima in complex problems.
High-dimensional feature selection is crucial for retaining informative features and discarding irrelevant ones.

Purpose of the Study:

To enhance the FOX algorithm's exploitation capabilities for high-dimensional feature selection.
To address the limitation of the basic FOX algorithm getting trapped in local optima.

Main Methods:

An improved FOX algorithm, FOX-GWO, was developed by integrating the Grey Wolf Optimizer (GWO).
An S-shaped transfer function was introduced to enable binary exploration during the search process.
Experiments were conducted on 18 datasets with varying dimensions.

Main Results:

FOX-GWO achieved superior performance across 18 datasets, with 83.33% average accuracy improvement.
The algorithm demonstrated a 61.11% improvement in reduced feature dimensionality.
An average fitness value improvement of 72.22% was observed, indicating efficient high-dimensional space exploration.

Conclusions:

FOX-GWO effectively mitigates the drawbacks of the basic FOX algorithm in feature selection.
The enhanced algorithm shows significant potential for advancing complex data analysis and improving model prediction accuracy.