用多目标优化构建更稳定、可解释的决策规则集。
MOSS: Multi-Objective Optimization for Stable Rule Sets
- 将稀疏性、准确性和稳定性统一为多目标优化框架
- 在稀疏规则集中快速计算准确率与稳定性的权衡边界
- 适合需要可解释且稳定的决策模型的研究者
我们提出MOSS，一种用于构建稳定决策规则集的多目标优化框架。该框架将可解释性中的三个关键标准——稀疏性、准确性和稳定性——整合到一个统一的多目标优化框架中。重要的是，MOSS使实践者能够快速评估稀疏规则集中准确率与稳定性之间的权衡，从而选择合适的模型。我们开发了一种专用的割平面算法，以高效计算这两个目标之间的帕累托前沿，其性能超越了商用优化求解器的处理能力。实验表明，MOSS在预测性能和稳定性方面均优于当前最先进的规则集成方法。
原文摘要 · Abstract (English)
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapidly compute the Pareto frontier between these two objectives, and our algorithm scales to problem instances beyond the capabilities of commercial optimization solvers. Our experiments show that MOSS outperforms state-of-the-art rule ensembles in terms of both predictive performance and stability.
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