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An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection.

Davies Segera1, Mwangi Mbuthia1, Abraham Nyete1

  • 1Department of Electrical and Information Engineering, University of Nairobi, Nairobi 30197, Kenya.

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|September 10, 2020
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A new hybrid algorithm, Excited-Adaptive Cuckoo Search-Intensification Dedicated Grey Wolf Optimization (EACSIDGWO), enhances feature selection for biomedical data. It improves classification accuracy, especially with small or high-dimensional datasets.

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Area of Science:

  • Biomedical Science
  • Machine Learning
  • Computational Biology

Background:

  • Feature selection is critical but challenging in high-dimensional and small-sample biomedical datasets.
  • Existing hybrid metaheuristic approaches show promise but can be improved by enhancing individual algorithms.
  • Optimizing feature selection is key for accurate biomedical data analysis and learning from limited instances.

Purpose of the Study:

  • To propose a novel hybrid metaheuristic algorithm, EACSIDGWO, for effective feature selection in biomedical science.
  • To enhance the performance of Adaptive Cuckoo Search (ACS) and Intensification Dedicated Grey Wolf Optimization (IDGWO) through adaptive parameter control inspired by DC circuits.
  • To demonstrate the algorithm's superiority in handling high-dimensional and small-sample biomedical datasets while maintaining high classification accuracy.

Main Methods:

  • Developed the Excited-Adaptive Cuckoo Search-Intensification Dedicated Grey Wolf Optimization (EACSIDGWO) algorithm.
  • Incorporated adaptive step size and nonlinear control parameters inspired by DC RC circuit responses.
  • Implemented a hybrid strategy with joint local exploitation in early stages and switched roles (ACS global exploration, IDGWO local exploitation) in later stages.
  • Tested EACSIDGWO on six UCI biomedical datasets for feature selection.

Main Results:

  • EACSIDGWO demonstrated comprehensive superiority over state-of-the-art methods (BACO, BGA, BPSO, EBCSA).
  • The algorithm achieved optimal feature selection and high classification accuracy, particularly for challenging datasets.
  • Experimental results validated through ranking methods and statistical analysis confirmed the algorithm's effectiveness.

Conclusions:

  • EACSIDGWO is a superior algorithm for tackling complex feature selection problems in biomedical science.
  • The adaptive parameter control strategy significantly enhances metaheuristic performance.
  • The proposed method offers a powerful tool for analyzing information-rich biomedical data, even with limited samples.