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在医疗图像处理机器学习中,培训和测试比率之间的权衡.

Muthuramalingam Sivakumar1, Sudhaman Parthasarathy2, Thiyagarajan Padmapriya2

  • 1Department of Computer Science and Engineering, Thiagarajar College of Engineering, Madurai, TamilNadu, India.

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概括

选择正确的火车-测试分割比率对于机器学习模型性能至关重要. 这项研究表明,不同的比率显著影响准确性,强调需要采取平衡的方法来避免过度装配或不足装配.

关键词:
医疗图像处理 医疗图像处理过度装配 过度装配 是一个问题.列车测试分成部分装饰不合适的 装饰不足的

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科学领域:

  • 计算机科学 计算机科学
  • 数据科学数据科学数据科学
  • 生物医学成像技术 生物医学成像技术

背景情况:

  • 人工智能 (AI) 和机器学习 (ML) 对于决策越来越重要.
  • 模型性能高度依赖于培训与测试数据的比例.
  • 有效的模型概括需要仔细考虑数据集的分割.

研究的目的:

  • 调查不同列车测试分割比对ML模型性能的影响.
  • 评估这些比率如何影响模型概括能力.
  • 为各种ML应用确定最佳的分割策略.

主要方法:

  • 用了BraTS 2013数据集进行分析.
  • 训练了多个ML模型,包括后勤回归,随机森林,K-最近邻居和支持向量机器.
  • 通过从60:40到95:05.5的列车测试分割比率进行了实验.

主要成果:

  • 在不同列车测试分割比率中观察到模型准确度的显著变化.
  • 证明了极端的比率可以导致过度装配或不足装配.
  • 强调了绩效指标,统计学意义和资源分配之间的权衡.

结论:

  • 选择最佳的列车测试分割比率对于可靠的ML模型开发至关重要.
  • 均衡模型性能和通用化需要仔细选择比率.
  • 调查结果为通过战略数据分割来提高ML应用程序的有效性提供了见解.