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相关实验视频

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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基于深度学习的决策树集,用于不完整的医疗数据集.

Chien-Hung Chiu1, Shih-Wen Ke2, Chih-Fong Tsai2

  • 1Division of Thoracic Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.

Technology and health care : official journal of the European Society for Engineering and Medicine
|May 30, 2023
PubMed
概括

本研究介绍了基于深度学习的决策树集 (DLDTE),以有效处理不完整的医疗数据集. 与缺少数据的现有方法相比,DLDTE实现了更高的分类准确性.

关键词:
数据科学是数据科学.分类器组合集团的组合.决策树 决策树是一个决定树.深度学习是一种深度学习.缺失的价值归算缺失的价值归算

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科学领域:

  • 机器学习 机器学习
  • 数据科学数据科学数据科学
  • 医疗信息学 医疗信息学

背景情况:

  • 在数据分析中,缺少属性值的不完整数据集是常见的.
  • 现有的解决方案包括归算或直接处理缺失的数据,通常使用决策树.

研究的目的:

  • 引入基于深度学习的决策树集 (DLDTE) 来分析不完整的数据集.
  • 为了利用深度学习策略来提高决策树的整体性能.

主要方法:

  • DLDTE利用了深度学习中的界限框和滑动窗口策略.
  • 该方法将不完整的数据集划分为决策树学习的子集.
  • 在两个医疗数据集上评估了性能,缺失率为10%-50%.

主要成果:

  • DLDTE证明了最高的分类准确性.
  • 它的表现优于基线决策树,平均归算,k-最近邻居归算和案例删除方法.

结论:

  • 拟议的DLDTE方法对于处理不完整的医学数据集是有效的.
  • 结果证实了DLDTE在各种缺失数据速率中的有效性.