基于Connectome的机器学习模型容易受到微妙的数据操纵的影响
Matthew Rosenblatt1, Raimundo X Rodriguez2, Margaret L Westwater3
1Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, CT 06510, USA.
Patterns (New York, N.Y.)
|July 31, 2023
概括
神经成像模型可以被欺骗性地操纵. 微小的数据变化大大改变了预测,破坏了研究完整性和对机器学习结果的信任.
科学领域:
- 神经成像是一种神经成像.
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 基于神经成像的预测模型正在进步.
- 模型可靠性 (对数据操纵的稳定性) 经常被忽视.
- 高可信度对于可靠的研究结果至关重要.
研究的目的:
- 为了研究微小数据操纵对机器学习预测使用功能连接组的影响.
- 评估错误地提高预测性能的方法.
- 为了评估旨在降低性能的对抗性噪音攻击.
主要方法:
- 使用功能连接体进行分析.
- 引入了数据操纵,包括性能增强方法和对抗性噪音攻击.
- 使用相似度指标 (r = 0.99) 比较原始和操纵的数据.
主要成果:
- 轻微的数据操纵显著改变了机器学习预测性能.
- 操纵的数据与原始数据非常相似 (r = 0.99).
- 这些操纵没有影响其他下游分析.
结论:
- 功能连接组数据可以微妙地修改,以实现所需的预测结果.
- 现有的对抗性攻击和新的增强攻击对模型可信度构成风险.
- 制定对策对于保持神经成像研究及其应用的完整性至关重要.
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