时间权重,因果推理和层次归因的集合,用于SHAP优化
Archana Salaria1, Manik Rakhra1, Nonita Sharma2
1School of Computer Science Engineering, Lovely Professional University.
Journal of visualized experiments : JoVE
|December 8, 2025
概括
本研究介绍了TCHSHAP,这是一种新的可解释人工智能 (XAI) 框架,通过时间加权和因果推理来优先考虑当前数据来提高模型解释性. 新方法提高了预测准确性和用户对复杂数据驱动应用程序的信心.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 数据驱动模型的日益复杂性需要先进的可解释的人工智能 (XAI) 技术.
- 传统的XAI方法往往难以解释数据中的动态关系.
- 可解释的人工智能 (XAI) 对人工智能驱动的预测的透明度和信任至关重要.
研究的目的:
- 提出一个新的 Ensemble SHapley 增量解释 (SHAP) 框架,TCHSHAP,旨在提高动态关系的可解释性.
- 整合时间权重,因果推理和层次归因,以增强模型解释.
- 验证TCHSHAP在提高透明度和可解释性方面的有效性,而不会影响模型性能.
主要方法:
- 开发TCHSHAP框架,包括时间加权 (指数式衰变),因果推理和层次归因.
- 应用数据预处理技术,包括一次热编码,最小-最大缩放和四分区间范围异常值的删除.
- 在作物产量数据集上使用随机森林模型进行实验性评估,将传统的SHAP与拟议的TCHSHAP进行比较.
主要成果:
- TCHSHAP模型显示平均预测从161.137 (传统SHAP) 提高到161.506,突出显示了时间和因果意义的有效性.
- 层次归因显示,农业特征对目标变量影响最大,其次是地理和环境因素.
- 拟议的方法提高了全球和本地解释性,从而增加了用户对模型预测的信心.
结论:
- 在复杂的数据驱动应用程序中,TCHSHAP有效地提高了透明度和可解释性,特别是在回归建模中.
- 该框架通过提供对模型行为和特征重要性更清晰的见解,增强了用户的信任.
- 在不牺牲预测性能的情况下,TCHSHAP为真实世界的场景中可解释的AI提供了可行的解决方案.
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