在特征归属中精确估计Shapley值的基于能量模型
IEEE transactions on pattern analysis and machine intelligence
|October 28, 2025
概括
基于能源的EmSHAP模型准确地估计了可解释AI的Shapley值. 这种方法有效地捕捉复杂的特征依赖性,提高在具有挑战性的数据环境中模型的可解释性.
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:
- 对于可解释的AI (XAI),Shapley值至关重要,它将特征贡献归因于模型输出.
- 由于数据中的复杂的条件依赖关系,估计沙普利值具有挑战性.
研究的目的:
- 建议EmSHAP (基于能源的Shapley值估计模型) 进行准确的Shapley值估计.
- 解决在复杂数据环境中估计Shapley值的挑战.
主要方法:
- 使用基于能源的模型 (EBM) 来建模复杂的分布,并估计条件概率.
- 采用Gated Recurrent Unit (GRU) 网络进行分区函数估计,捕获长期依赖.
- 整合一个动态掩盖机制,以提高稳定性和准确性.
主要成果:
- 与现有方法相比,EmSHAP在沙普利估值中显示出更高的准确性.
- 拟议的方法对复杂的数据集具有更好的可扩展性.
- 理论分析证实了误差界限,实际案例研究验证了性能.
结论:
- 在XAI中,EmSHAP为准确的Shapley值估计提供了一个有效的解决方案.
- 结合EBM和GRU可以提高复杂的机器学习模型的解释性.
- EmSHAP为特征归属提供了一个可扩展和强大的方法.
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