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Multi-objective feature selection includes two objectives, which choose the relevant features for minimal number and obtain the better performance of classification accuracy. Binary Particle Swarm Optimization is intelligent optimizing algorithm with binary bits position and suitable for feature selection. A BPSO algorithm is proposed with a new velocity updating strategy with learning from neighbors in this chapter. Meanwhile, the novel algorithm utilizes a new transfer function for the updating of the bits position of particles. The multi-objective feature selection is done with the proposed Binary PSO, which employs the Pareto front to assist decision-makers for selection of the feature subset. The experiments are conducted with 10 datasets of the UCI repository with machine learning, and the experimental results demonstrate that the proposed BPSO outperforms three other BPSO according to the hypervolume indicator.

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An Binary Particle Swarm Optimization with Learning from Neighbors for Multi-objective Feature Selection

  • 会议论文

  • 发表日期：

    2025年

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  • Xiaofeng Wang,
  • Jianhua Liu,
  • Yuxiang Chen,
  • Jian Zhu,
  • Zhichun Xie

摘要

Multi-objective feature selection includes two objectives, which choose the relevant features for minimal number and obtain the better performance of classification accuracy. Binary Particle Swarm Optimization is intelligent optimizing algorithm with binary bits position and suitable for feature selection. A BPSO algorithm is proposed with a new velocity updating strategy with learning from neighbors in this chapter. Meanwhile, the novel algorithm utilizes a new transfer function for the updating of the bits position of particles. The multi-objective feature selection is done with the proposed Binary PSO, which employs the Pareto front to assist decision-makers for selection of the feature subset. The experiments are conducted with 10 datasets of the UCI repository with machine learning, and the experimental results demonstrate that the proposed BPSO outperforms three other BPSO according to the hypervolume indicator.