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Evolutionary binary feature selection using adaptive ebola optimization search algorithm for high-dimensional

Olaide N Oyelade1, Jeffrey O Agushaka2, Absalom E Ezugwu2

  • 1Department of Computer Science, Faculty of Physical Sciences, Ahmadu Bello University, Zaria, Nigeria.

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|March 17, 2023
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Summary
This summary is machine-generated.

This study introduces a novel hybrid binary optimization for effective feature selection in high-dimensional datasets. The proposed method, HBEOSA-FFA, achieves superior classification accuracy and performance compared to existing algorithms.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • High-dimensional datasets from diverse sources pose challenges for feature selection.
  • Existing binary optimization methods and their hybrid variants often inherit limitations, impacting feature selection quality.
  • Effective feature selection is crucial for improving classifier performance and data analysis.

Purpose of the Study:

  • To propose a novel hybrid binary optimization approach for efficient feature selection in high-dimensional datasets.
  • To introduce a sub-population selective mechanism for dynamic assignment to a 2-level optimization process.
  • To investigate the influence of nested transfer (NT) functions on optimization performance.

Main Methods:

  • Developed a novel hybrid binary optimization with a sub-population selective mechanism for a 2-level optimization process.
  • Employed the Binary Ebola Optimization Search Algorithm (BEOSA) for level-1 mutation.
  • Investigated Simulated Annealing (SA) and Firefly (FFA) algorithms for level-2 optimization, resulting in HBEOSA-SA and HBEOSA-FFA.
  • Introduced and evaluated nested transfer (NT) functions, creating variants HBEOSA-SA-NT and HBEOSA-FFA-NT.

Main Results:

  • The proposed hybrid methods (HBEOSA-SA, HBEOSA-FFA, HBEOSA-SA-NT, HBEOSA-FFA-NT) outperformed the base BEOSA algorithm.
  • HBEOSA-FFA achieved a classification accuracy of 0.995 on large-scale datasets.
  • Comparative analysis demonstrated performance variability across different dataset scales, with HBEOSA-FFA showing strong results.

Conclusions:

  • The novel hybrid binary optimization approach effectively addresses feature selection challenges in high-dimensional data.
  • The sub-population selective mechanism and nested transfer functions contribute to improved optimization performance.
  • The proposed methods offer a promising solution for enhancing feature selection accuracy and efficiency.