自动XAI:一种超学习方法,用于推解释技术
1Institute of Computer Science, University of Tartu, Tartu, Estonia. radwa.elshawi@ut.ee.
Scientific reports
|November 25, 2025
概括
AutoXAI自动化了对表格数据的机器学习解释技术的选择. 这种元学习框架通过推基于用户定义的指标的最佳方法来提高模型的解释性.
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:
- 对于机器学习模型来说,选择最佳的全局解释技术是具有挑战性的,因为缺乏通用方法.
- 目前的选择过程是手动的,低效的,容易产生偏见,阻碍了模型的解释性.
研究的目的:
- 引入AutoXAI,这是一个元学习框架,用于自动化对表格数据进行监督学习的全球解释技术的建议.
- 通过将技术建议与用户定义的定量指标对齐来优化模型的解释性.
主要方法:
- AutoXAI利用最佳运输来识别具有相似分布的数据集.
- 采用多目标优化来根据所选的指标选择解释方法.
- 该框架支持像LIME,Anchor,RuleFit和RuleMatrix这样的无模型技术.
主要成果:
- 在21个基准数据集中的19个中,AutoXAI的建议与表现最好的解释技术相匹配.
- 该框架与静态启发式启发式计算相比,表现出更高的性能.
- 强度实验显示,在90%以上的场景中,在噪声干扰下,一致的建议.
结论:
- AutoXAI提供了一个可扩展的,数据驱动的解决方案,用于选择解释技术,减少手工工作和增加对模型决策的信任.
- 该框架的适应性和耐噪声性使其适用于医疗保健和入侵检测中的关键应用.
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