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用SHAP解释人类活动识别:通过扰乱和定量措施验证洞察力

Felix Tempel¹, Espen Alexander F Ihlen², Lars Adde³

¹Faculty of Informatics, Norwegian University of Science and Technology, Trondheim, Norway.

Computers in biology and medicine

|February 23, 2025

概括

本研究解释了使用夏普利添加式扩展 (SHAP) 来识别人类活动 (HAR) 的图形卷积网络 (GCN). SHAP识别了关键的身体点,改善了关键应用程序的模型解释性.

科学领域:

计算机科学计算机科学
人工智能的人工智能
生物医学工程生物医学工程

背景情况:

人类活动识别 (HAR) 对高风险应用至关重要.
图形卷积网络 (GCNs) 对于使用骨架数据的 HAR 有效.
在HAR中解释GCN决策仍然是一个挑战.

研究的目的:

用SHapley添加式扩展 (SHAP) 来解释HAR中的GCN决策.
评估特定的身体关键点对HAR模型预测的影响.
在医疗保健等关键应用中增强模型可信度.

主要方法:

应用SHAP来解释关于脑 (CP) 分类的GCN和NTU RGB+D 60数据集.
引入了一种新的扰动方法来评估身体关键点的影响.
使用知情扰动与随机扰动来验证SHAP解释.

主要成果:

SHAP识别的重要身体关键点显著影响了HAR模型的准确性,特异性和灵敏性.
扰动分析证实了身体关键点对预测结果的影响.
SHAP提供了对HAR中GCN的特征贡献的详细见解.

结论:

SHAP有效地解释了HAR任务中的GCN,突出了有影响力的身体关键点.
这些发现支持开发更可解释和可信的HAR模型.
这项研究对需要可靠的活动识别的高风险应用有影响.

关键词:

可解释的人工智能全国CNN是什么意思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人类活动识别人类活动识别衡量指标衡量指标衡量指标衡量指标扰动扰动扰动这就是 SHAP SHAP 的意思.形状GCNCN 的意思在XAI,XAI就是XAI.

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