将一个黑盒子分成一个可解释模型的混合物:路由,解释,重复
Shantanu Ghosh1, Ke Yu2, Forough Arabshahi3
1Department of Electrical and Computer Engineering, Boston University, MA, USA.
概括
本研究引入了一种新的机器学习 (ML) 方法,该方法将灵活的黑盒模型与可解释的组件相结合. 该方法反复地提取可解释的模型,改善概念理解和模型性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 可解释的人工智能
背景情况:
- 机器学习模型设计面临的是可解释性和性能之间的权衡.
- 黑盒模型提供了灵活性,但缺乏固有的解释性,而可解释模型是可解释的,但通常性能较差.
- 目前的方法要么从可解释模型开始,要么解释黑子模型后期.
研究的目的:
- 弥合黑盒模型的后期解释和内在可解释模型的构建之间的差距.
- 开发一种方法,从黑盒模型中提取可解释的组件,而不会影响性能.
主要方法:
- 提出了一种新的"路线,解释和重复"方法,从黑盒模型开始.
- 代地,可解释模型 (可解释专家混合 - MoIE) 使用第一阶逻辑 (FOL) 进行样本解释.
- 剩余网络处理剩余的样本,在剩余网络上重复这个过程.
主要成果:
- 该方法通过MoIE识别了多样化,实例特定的概念,并具有高度的完整性.
- 它有效地将难以解释的样本隔离到剩余网络中.
- 在测试时间干预中超越可解释的设计模型,并纠正黑盒模型快捷方式.
结论:
- 提出的方法成功地将可解释性和性能与机器学习相结合.
- 它提供了一种强大的方法,通过提取可解释的知识来理解和改进复杂的ML模型.
- 该技术增强了模型的稳定性,并纠正了学习的偏见或快捷方式.
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