学习可解释的规则,用于可扩展的数据表示和分类
IEEE transactions on pattern analysis and machine intelligence
|October 31, 2023
概括
我们介绍了基于规则的表示学习器 (RRL),这是一个新的分类器,可以学习数据表示和分类的可解释规则. RRL实现了高精度和可扩展性,超过了现有的可解释方法.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 基于规则的模型,如决策树,具有很高的解释性,但很难在大型数据集上进行优化.
- 现有的提高性能的方法,如集体方法或模糊规则,往往会损害模型的解释性.
- 在基于规则的机器学习模型中,可扩展性和可解释性往往相互排斥.
研究的目的:
- 提出一种新的分类器,即基于规则的表示学习器 (RRL),可以实现良好的可扩展性和可解释性.
- 为非可区分的基于规则的模型开发有效的培训方法.
- 在基于规则的框架内实现连续特征的端到端分类.
主要方法:
- 开发了基于规则的表示学习器 (RRL),这是一种学习可解释的非模糊规则的分类器.
- 提出了一种新的训练方法,即梯度接种,通过将它们投射到连续空间中来优化使用梯度下降的离散模型.
- 设计了新的逻辑激活功能,以提高RRL的可扩展性,并实现端到端的功能分离.
主要成果:
- 与竞争性可解释方法相比,RRL在10个小数据集和4个大数据集中表现优越.
- 拟议的梯度接种方法有效地训练了不可差异的RRL模型.
- 新的逻辑激活函数提高了RRL的可扩展性,并允许端到端的功能离散.
结论:
- RRL为分类任务提供了一个可扩展和可解释的解决方案,克服了传统基于规则的模型的局限性.
- 梯度接种训练方法和逻辑激活功能是实现RRL有效性的关键创新.
- 对于各种应用,RRL提供了灵活性来平衡分类准确性和模型复杂性.
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