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Enhanced Ali Baba and the forty thieves algorithm for feature selection.

Malik Braik1

  • 1Department of Computer Science, Al-Balqa Applied University, Salt, Jordan.

Neural Computing & Applications
|November 21, 2022
PubMed
Summary
This summary is machine-generated.

This study enhances feature selection (FS) using improved Ali Baba and the Forty Thieves (AFT) algorithms. The Binary Multi-layered AFT (BMAFT) demonstrated superior performance in classification tasks, offering faster convergence and higher accuracy.

Keywords:
AFT algorithmClassificationFeature selectionOptimization

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Feature Selection (FS) is crucial for improving classification model performance by reducing high-dimensional data.
  • Traditional methods often struggle with optimal feature reduction in complex datasets.
  • Meta-heuristic algorithms show promise for enhancing classification rates in high-dimensional scenarios.

Purpose of the Study:

  • To address limitations of the basic Binary Ali Baba and the Forty Thieves (AFT) algorithm, such as premature convergence and suboptimal search performance.
  • To introduce and evaluate three enhanced versions of the Binary AFT (BAFT) algorithm for feature selection problems.
  • To identify the most effective AFT variant for optimizing classification performance on complex datasets.

Main Methods:

  • A binary version of the human-based Ali Baba and the Forty Thieves (AFT) meta-heuristic was applied to feature selection.
  • Three enhanced AFT versions were developed: Binary Multi-layered AFT (BMAFT), Binary Elitist AFT (BEAFT), and Binary Self-adaptive AFT (BSAFT).
  • The algorithms were evaluated on twenty-four diverse feature selection problems from multiple repositories.

Main Results:

  • All proposed enhanced AFT algorithms demonstrated significant improvements over the basic BAFT in terms of convergence speed and solution accuracy.
  • The Binary Multi-layered AFT (BMAFT) emerged as the most competitive algorithm.
  • BMAFT consistently achieved the best performance scores compared to other competing algorithms and enhanced versions.

Conclusions:

  • Enhanced versions of the AFT algorithm effectively mitigate the limitations of the basic BAFT for feature selection.
  • The BMAFT algorithm offers a robust and high-performing solution for feature selection in high-dimensional datasets.
  • The proposed BMAFT algorithm represents a significant advancement in meta-heuristic approaches for optimizing classification models.