计算最优决策集的可扩展两阶段方法
A Scalable Two Stage Approach to Computing Optimal Decision Sets
摘要：
机器学习（ML）在现代生活中无处不在。由于它被部署在影响我们隐私和安全的技术中，因此了解其决策背后的推理通常至关重要，从而避免了对可解释的人工智能的需求。基于规则的模型，例如决策树、决策列表和决策集，通常被认为是最可解释的。最近的工作使用命题可满足性（SAT）求解（及其优化变体）来生成最小大小的决策集。由于这些早期方法的实际可扩展性有限，本文提出了一种新的方法来学习最小大小的决策集，通过枚举目标决策集的各个规则彼此独立，然后解决集合覆盖问题来选择规则的子集。该方法利用现代最大可满足性和整数线性规划技术。在广泛的公开数据集上的实验证明了新方法在基于SAT的决策集学习中的优势。
英文摘要：
Machine learning (ML) is ubiquitous in modern life. Since it is being deployed in technologies that affect our privacy and safety, it is often crucial to understand the reasoning behind its decisions, warranting the need for explainable AI. Rule-based models, such as decision trees, decision lists, and decision sets, are conventionally deemed to be the most interpretable. Recent work uses propositional satisfiability (SAT) solving (and its optimization variants) to generate minimum-size decision sets. Motivated by limited practical scalability of these earlier methods, this paper proposes a novel approach to learn minimum-size decision sets by enumerating individual rules of the target decision set independently of each other, and then solving a set cover problem to select a subset of rules. The approach makes use of modern maximum satisfiability and integer linear programming technologies. Experiments on a wide range of publicly available datasets demonstrate the advantage of the new approach over the state of the art in SAT-based decision set learning.
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