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How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
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猎物捕获增强了哈里斯·霍克斯优化器,用于在高维医学数据中基于包装的特征选择.

Mohammed Batis1, Yi Chen1, Lei Liu2

  • 1Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou, 325035, China.

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概括

猎物捕获哈里斯霍克斯优化器 (PCHHO) 增强了对高维数据的特征选择. 它的二进制变体bPCHHO显著减少了分类错误和计算时间,同时选择了更少的功能.

关键词:
交叉式车辆交叉式车辆功能选择 功能选择哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈哈高维的高维空间突变突变是一种突变.优化优化 优化优化捕获猎物捕获猎物的捕获

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科学领域:

  • 优化算法优化算法
  • 计算智能是一种计算智能.
  • 数据科学是数据科学.

背景情况:

  • 哈里斯·霍克斯优化器 (HHO) 对于特征选择是有效的,但在高维数据,局部最佳值和计算成本方面存在困难.
  • 在HHO的猎物捕获机制的局限性可能会阻碍其在复杂数据集上的表现.

研究的目的:

  • 引入了一种增强的HHO算法,即猎物捕获哈里斯霍克斯优化器 (PCHHO),以提高猎物捕获能力.
  • 在高维数据集上开发二进制变体 (bPCHHO) 用于基于包装的特征选择.

主要方法:

  • PCHHO集成交叉和突变运营商,以提高探索性开发能力.
  • 在CEC2017基准套件上对HHO和其他元启发术进行了PCHHO的评估.
  • bPCHHO在15个高维医学数据集上进行了测试,与6个二进制元启发器相对应.

主要成果:

  • 在CEC2017基准套件上,PCHHO表现出卓越的表现.
  • 与bHHO相比,bPCHHO的分类错误减少了77%,计算时间减少了8%,选择的功能减少了73%.
  • 统计测试 (威尔科克森签名等级,弗里德曼) 证实了显著的性能改善.

结论:

  • 在基准优化和特征选择方面,PCHHO和bPCHHO表现出色.
  • bPCHHO对于在高维度医疗数据上基于包装的特征选择非常有效.
  • 改进的算法为数据分析中的实际应用提供了有前途的潜力.