对神经时间序列分类器的可解释AI特征归属验证中扰动方法的全面分析
Ilija Šimić1,2, Eduardo Veas3,4, Vedran Sabol4
1Graz University of Technology, Graz, Austria. isimic@know-center.at.
Scientific reports
|July 22, 2025
概括
这项研究引入了一种新指标,即一致性-大小-指数,用于验证可解释AI (XAI) 中的特征归因方法. 它为人工智能模型解释提供了改进的忠实性评估,特别是对于时间序列数据.
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:
- 可解释的人工智能 (XAI) 在医学和金融等高风险领域至关重要.
- 特性归属方法 (AMs) 是用于识别AI模型中具有影响力的特征的常见方法.
- 目前对AM的验证指标显示出缺陷,特别是在时间序列数据方面.
研究的目的:
- 解决对人工智能解释方法严格验证的需求.
- 引入一种新的指标,用于对特征重要性归因的忠实评估.
- 开发一种适应的方法,用于对AMs进行强有力的忠实性评估.
主要方法:
- 引入用于AM验证的一致性-大小-指数指标.
- 开发一种适应的方法,使用多种扰动方法进行忠实性评估.
- 对时间序列数据的扩展评估,考虑扰动方法和区域大小.
主要成果:
- 一致性-大小指数有助于更准确地评估特征的重要性.
- 扰乱方法和区域大小显著影响对时间序列数据的AM评估.
- 基于广泛的评估,为未来的AM忠实性评估提供了指导方针.
结论:
- 拟议的指标和方法提高了AI解释的可靠性.
- 该研究为在时间序列分析中评估AM提供了实用见解.
- 在多变量时间序列示例上演示方法验证了它的实用性.
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