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An Improved Feature Selection Algorithm for Harris Hawk optimization Based on Hybrid Strategy

Published: 28 June 2024 Publication History

Abstract

Feature selection is a popular issue in machine learning, which involves selecting the most effective features from the original features to reduce the dimensionality of the dataset. It is an important means to improve the performance of learning algorithms and a key data preprocessing step in pattern recognition. An improved feature selection algorithm for Harris Hawk Optimization (HHO) is proposed to address the shortcomings of the algorithm. First, self-adaptive elite opposition-based learning strategy is introduced in the exploration phase to initialize the position of Harris hawk population. Secondly, using logarithmic inertia weight to improve the update formula of escape energy, introducing the number of iterations into the jump distance, adjusting the search distance of HHO using step size adjustment parameters, and balancing exploration and development capabilities; On this basis, an improved HHO algorithm was designed to avoid the HHO algorithm falling into local optima. Then, a quadratic transfer function was introduced to update the binary and population positions of the improved HHO algorithm, and an IHHO algorithm was designed to solve feature selection problems. Experiments were conducted on five common benchmark datasets including Wine, Segment, Spectfheart, Sonar, and Ionosphere, and the results showed that the improved algorithm can effectively improve classification accuracy and select the optimal features.

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  1. An Improved Feature Selection Algorithm for Harris Hawk optimization Based on Hybrid Strategy

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ICRSA '23: Proceedings of the 2023 6th International Conference on Robot Systems and Applications
September 2023
335 pages
ISBN:9798400708039
DOI:10.1145/3655532
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Association for Computing Machinery

New York, NY, United States

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Published: 28 June 2024

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Author Tags

  1. Binary
  2. Feature selection
  3. Fitness function
  4. Harris Hawk optimization algorithm
  5. Swarm intelligence

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