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Updated: Jun 27, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A Chaos-Enhanced Binary Newton-Raphson Optimizer for High-Dimensional Sensor Data Feature Selection.

Abdelmonem M Ibrahim1, Doaa A Fakhry2,3, Fares Al-Shargie4

  • 1Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut Branch, Assiut 71524, Egypt.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

A new Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO) effectively reduces features in high-dimensional biomedical data. This method enhances classification performance and biomarker discovery, outperforming existing techniques.

Keywords:
chaotic mapsclassificationfeature selectionmetaheuristicsneuroimagingoptimization algorithmswrapper approach

Related Experiment Videos

Last Updated: Jun 27, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biomedical data analysis
  • Machine learning
  • Computational biology

Background:

  • High-dimensional data presents challenges in feature selection, including redundancy and poor generalization.
  • Interpretable biomarker discovery is essential for understanding complex biological systems.
  • Existing feature selection methods may struggle with convergence and exploration in complex search spaces.

Purpose of the Study:

  • To introduce a novel wrapper-based feature selection method, the Binary Chaos-Enhanced Newton-Raphson-Based Optimizer (BCNRBO).
  • To enhance the exploration capabilities and prevent premature convergence in feature selection algorithms.
  • To evaluate the effectiveness of BCNRBO in reducing feature dimensionality and improving classification performance for sensor-derived biomedical data.

Main Methods:

  • Developed BCNRBO, integrating chaotic dynamics, a Hamming-distance-based Dynamic Potential mechanism, and a novel binary transfer function.
  • Evaluated BCNRBO on 26 benchmark datasets using K-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM) classifiers.
  • Compared BCNRBO's performance against competing binary metaheuristic methods for feature selection.

Main Results:

  • BCNRBO consistently achieved competitive or superior classification performance compared to existing methods.
  • The proposed method selected fewer features, demonstrating superior feature reduction capabilities across all tested classifiers.
  • BCNRBO secured top Friedman rankings for DT, NB, and SVM, indicating its overall effectiveness and robustness.
  • Statistical tests confirmed significant improvements of BCNRBO over competing methods in most pairwise comparisons.

Conclusions:

  • BCNRBO is a highly effective feature selection strategy for high-dimensional sensor-derived biomedical and neurorehabilitation data.
  • The method's ability to reduce redundancy and improve generalization supports the discovery of compact and reliable digital biomarkers.
  • BCNRBO offers a promising approach for advancing machine learning applications in biomedical research and clinical practice.