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A hybrid feature selection model based on butterfly optimization algorithm: COVID-19 as a case study.

Ibrahim M El-Hasnony1, Mohamed Elhoseny1,2, Zahraa Tarek1

  • 1Faculty of Computers and Information Mansoura University Mansoura Egypt.

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|September 13, 2021
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Summary
This summary is machine-generated.

This study introduces a hybrid feature selection (FS) method, BOAPSO, combining butterfly optimization algorithm (BOA) and particle swarm optimization (PSO). BOAPSO significantly improves classification accuracy and reduces feature selection time compared to existing methods.

Keywords:
COVID‐19butterfly optimization algorithmdata classificationfeature selectionparticle swarm optimization

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Area of Science:

  • Machine Learning
  • Computational Intelligence
  • Data Science

Background:

  • High-dimensional datasets pose challenges for algorithm accuracy and efficiency due to irrelevant features.
  • Traditional feature selection (FS) methods often involve time-consuming exhaustive searches.
  • Optimization techniques offer potential for robust and efficient FS systems.

Purpose of the Study:

  • To develop a novel hybrid feature selection (FS) approach to address the limitations of existing methods.
  • To enhance the precision and convergence speed of the butterfly optimization algorithm (BOA).
  • To improve the efficiency of FS in high-dimensional datasets.

Main Methods:

  • A hybrid feature selection (FS) approach, BOAPSO, is proposed, integrating the butterfly optimization algorithm (BOA) with particle swarm optimization (PSO) within a wrapper framework.
  • BOA is initialized using a one-dimensional cubic map with a non-linear parameter control technique.
  • PSO is combined with BOA to enhance global optimization capabilities.

Main Results:

  • The proposed BOAPSO method was evaluated on 25 datasets, including a COVID-19 dataset, assessing classification precision, number of selected features, and computational time.
  • BOAPSO demonstrated superior performance in enhancing classification precision and minimizing the number of selected features compared to PSO, BOA, and Grey Wolf Optimizer (GWO).
  • Experimental results showed significant improvements in accuracy (91.07% for BOAPSO vs. 87.2%-87.8% for others) and a reduction in average selected features (5.7 for BOAPSO vs. 18.05-23.1 for others).

Conclusions:

  • The hybrid BOAPSO approach effectively enhances classification precision and reduces computational time in feature selection.
  • The method proves superior to existing techniques like PSO, BOA, and GWO for high-dimensional data analysis.
  • BOAPSO offers a robust and efficient solution for feature selection, particularly in complex datasets like those found in medical research.