多类分类的真无序概率规则集
Truly Unordered Probabilistic Rule Sets for Multi-class Classification
摘要：
规则集学习已经研究了很长时间，最近由于需要可解释的模型而经常被重新审视。然而，现有的规则学习方法仍存在一些不足：1）大多数方法需要二进制特征矩阵作为输入，而直接从数值型变量学习规则的研究还不够深入; 2）现有的规则学习方法在规则之间强加了显式或隐式的顺序，这损害了规则的可解释性;以及3）当前不存在用于学习多类目标变量的概率规则集的方法（仅存在一种用于概率规则列表的方法）。我们提出TURS，真正无序的规则集，它解决了这些缺点。我们首先正式学习真正无序规则集的问题。为了解决由重叠规则引起的冲突，即，实例覆盖的多个规则，我们提出了一种新的方法，利用我们的规则集的概率属性。接下来，我们开发了一个两阶段的启发式算法，学习规则集仔细增长的规则。一个重要的创新是，我们使用一个代理分数，以考虑到全局潜力的规则集学习时，当地的规则。最后，我们经验证明，与非概率和（显式或隐式）有序的最先进的方法相比，我们的方法学习的规则集不仅具有更好的可解释性，而且具有更好的预测性能。
英文摘要：
Rule set learning has long been studied and has recently been frequently revisited due to the need for interpretable models. Still, existing methods have several shortcomings: 1) most recent methods require a binary feature matrix as input, while learning rules directly from numeric variables is understudied; 2) existing methods impose orders among rules, either explicitly or implicitly, which harms interpretability; and 3) currently no method exists for learning probabilistic rule sets for multi-class target variables (there is only one for probabilistic rule lists). We propose TURS, for Truly Unordered Rule Sets, which addresses these shortcomings. We first formalize the problem of learning truly unordered rule sets. To resolve conflicts caused by overlapping rules, i.e., instances covered by multiple rules, we propose a novel approach that exploits the probabilistic properties of our rule sets. We next develop a two-phase heuristic algorithm that learns rule sets by carefully growing rules. An important innovation is that we use a surrogate score to take the global potential of the rule set into account when learning a local rule. Finally, we empirically demonstrate that, compared to non-probabilistic and (explicitly or implicitly) ordered state-of-the-art methods, our method learns rule sets that not only have better interpretability but also better predictive performance.
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。
