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相关概念视频

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生物医学分类任务的动态Coati优化算法

Essam H Houssein1, Nagwan Abdel Samee2, Noha F Mahmoud3

  • 1Faculty of Computers and Information, Minia University, Minia, Egypt.

Computers in biology and medicine
|July 19, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种动态的Coati优化算法 (DCOA),用于医疗数据集中的有效特征选择. DCOA通过识别和删除冗余数据来提高机器学习分类器的性能,从而提高诊断准确性.

关键词:
科蒂优化算法 科蒂优化算法动态对立的动态对立选择功能选择功能选择.机器学习是机器学习.医学诊断 医学诊断 医学诊断 医学诊断 医学诊断k-最近的邻居

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科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 生物信息学是一种生物信息学.

背景情况:

  • 医疗数据集往往包含冗余和无关的特征,增加了维度.
  • 高维度会对机器学习分类器的性能产生负面影响,特别是对于k-Nearest Neighbors (kNN) 等算法.
  • 特性选择是解决医疗数据分析中的这些挑战的关键技术.

研究的目的:

  • 提出一种新的动态特征选择方法,以提高医疗诊断分类器的性能.
  • 通过从医疗数据集中选择基本属性来增强k-最近邻居 (kNN) 分类器.
  • 引入动态Coati优化算法 (DCOA) 进行高效和自适应的特征选择.

主要方法:

  • 开发了Coati优化算法 (DCOA) 的动态版本,其中包含动态对立候选解决方案.
  • 实施DCOA作为一个特征选择技术,在优化过程中,特征被代引入.
  • 使用CEC'22测试套件和九个不同的医疗数据集,对DCOA与原来的COA和其他七个元启发算法进行了评估.

主要成果:

  • 拟议的DCOA与七个知名的元启发算法相比,表现优越.
  • 实现了整体准确率为89.7%,特征选择将数据减少了24%.
  • 报告的高性能指标:灵敏度为93.35%,特异性为96.81%,精度为93.90%.

结论:

  • 动态Coati优化算法 (DCOA) 是医疗数据集的有效特征选择方法.
  • DCOA提高了分类器的性能,而不需要参数调整,提供了一个实际的优势.
  • 该方法显示了在基于机器学习的医疗应用中提高诊断准确性的巨大潜力.