提出新方法生成高覆盖率、少规则且准确的分类规则集。
Learning High Coverage Discriminative Parsimonious Rulesets
- 基于子模最大化设计算法，保证规则集覆盖率
- 相比最优现有方法，平均覆盖率提升2.5倍以上
- 适合需要可解释性与高效决策的场景
基于条件-结果规则表示的学习系统具有天然可解释性，是当前人工智能研究的重要方向。这类规则集的核心目标是兼具高判别力与可解释性。然而，现有最先进算法虽注重预测准确率，却常在覆盖度和规则简洁性等可解释性指标上表现不足。为此，本文提出CDPR方法，旨在为分类问题生成高准确率且可解释的规则集。据我们所知，这是首次系统性构建此类方法的研究。本工作引入两种基于子模最大化原理的算法，不仅能提供覆盖率的可证明保证，还能生成判别性强且规则简洁的规则集。实验表明，所学规则集在准确率和可解释性上均更优，平均覆盖率比次优算法高出2.5倍以上。
原文摘要 · Abstract (English)
Learning systems based on IF-THEN rule representations readily offer interpretability, making them a crucial focus in contemporary AI research. A key objective for such rule sets is to achieve both high discriminative power and interpretability. While existing state-of-the-art algorithms implicitly prioritize predictive accuracy, they often fall short on one or more quality metrics that ensure interpretability, such as coverage and parsimony of rule sets. Motivated by this, this paper propose the development of CDPR, which aims to create highly accurate and interpretable rule sets for classification problems. To the best of our knowledge, this represents the first attempt to establish such an approach. In this study, we introduce two algorithms rooted in submodular maximization, which not only provide provable guarantees on coverage but also yield rule sets that are both discriminative and parsimonious. We empirically demonstrate that rule sets learned through our approaches achieve higher accuracy and interpretability and has more than a 2.5-fold improvement in average coverage rates when compared to the next best algorithm.
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