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IHBOFS:一种以仿生学为灵感的混合繁殖优化算法,用于高维特征选择.

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  • 1School of Computer Science, Hubei University of Technology, No. 28 Nanli Road, Hongshan District, Wuhan 430068, China.

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概括
此摘要是机器生成的。

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精英的反对派基于学习的学习.一个好的点数集.高维特征选择的高维特征选择杂交育种优化优化 杂交育种优化

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科学领域:

  • 计算智能是一种计算智能.
  • 数据科学数据科学数据科学
  • 优化算法 优化算法

背景情况:

  • 由于数据的爆炸性增长,有效的数据预处理至关重要.
  • 进化和群集智能算法在特征选择方面表现有前途,但在大规模问题上却难以应对.
  • 过早的融合和有限的探索阻碍了现有算法的性能.

研究的目的:

  • 提出IHBOFS,一个新的仿生学灵感优化框架,用于增强特征选择.
  • 解决现有算法在大规模,高维特征选择中的局限性.
  • 为了提高数据预处理任务的性能和稳定性.

主要方法:

  • 开发了IHBOFS,整合了Good Point Set和精英基于对立的学习以实现多样化的初始化.
  • 实施了适应性开发-勘探平衡策略,以缓解分群的过早融合.
  • 扩展IHBOFS,用于离散特征选择问题的连续到离散映射.

主要成果:

  • 关于CEC2022基准函数的废除研究验证了拟议策略的有效性.
  • 在六个现实数据集上,IHBOFS的平均分类准确率为92.57%.
  • 对比实验表明IHBOFS的优势超过九个元启发式方法,包括HHO和ACO.

结论:

  • IHBOFS有效地提高了高维数据集的特征选择性能和稳定性.
  • 综合适应策略成功地缓解了过早的融合,并改善了勘探.
  • IHBOFS为数据科学中复杂的特征选择任务提供了强大而优异的解决方案.