具有学习关系特征的可微规则归纳
Differentiable Rule Induction with Learned Relational Features
摘要：
基于规则的决策模型由于其可解释性而具有吸引力。然而，现有的规则归纳方法往往会导致长，从而更少的可解释的规则模型。这个问题通常可以归因于缺乏适当的表达词汇，即，在决策模型中用作文字的相关谓词。大多数现有的规则归纳算法都假设预定义的字面量，从而自然地将字面量的定义与规则学习阶段分离开来。相比之下，我们提出了关系规则网络（R2N），这是一种神经架构，可以学习表示数值输入特征之间线性关系的文字以及使用它们的规则。这种方法通过将文字学习直接与规则学习以端到端可区分的方式耦合，为增加诱导决策模型的表达能力打开了大门。在基准测试任务中，我们证明了这些学习到的文字足够简单，可以保持可解释性，但可以提高预测精度，并提供比最先进的规则归纳算法更简洁的规则集。
英文摘要：
Rule-based decision models are attractive due to their interpretability. However, existing rule induction methods often result in long and consequently less interpretable rule models. This problem can often be attributed to the lack of appropriately expressive vocabulary, i.e., relevant predicates used as literals in the decision model. Most existing rule induction algorithms presume pre-defined literals, naturally decoupling the definition of the literals from the rule learning phase. In contrast, we propose the Relational Rule Network (R2N), a neural architecture that learns literals that represent a linear relationship among numerical input features along with the rules that use them. This approach opens the door to increasing the expressiveness of induced decision models by coupling literal learning directly with rule learning in an end-to-end differentiable fashion. On benchmark tasks, we show that these learned literals are simple enough to retain interpretability, yet improve prediction accuracy and provide sets of rules that are more concise compared to state-of-the-art rule induction algorithms.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
