防止产生不一致的分类规则集
Preventing the Generation of Inconsistent Sets of Classification Rules
摘要：
近年来，对可解释分类模型的兴趣不断增长。改进基于规则的分类模型的可解释性的方法之一是使用规则集（无序集合），而不是规则列表（有序集合）。与集合相关的问题之一是，多个规则可能覆盖单个实例，但为它预测不同的类，因此需要冲突解决策略。在这项工作中，我们提出了两个算法，能够找到特征空间区域内的任何创建的规则将与现有的规则一致，防止出现不一致。我们的算法不生成分类模型，而是为了增强这样做的算法，例如学习分类器系统。这两种算法的描述和分析完全从理论的角度来看，因为我们还没有修改模型生成算法，将我们提出的解决方案。这项工作提出了新颖的冲突避免策略，而不是冲突解决策略。
英文摘要：
In recent years, the interest in interpretable classification models has grown. One of the proposed ways to improve the interpretability of a rule-based classification model is to use sets (unordered collections) of rules, instead of lists (ordered collections) of rules. One of the problems associated with sets is that multiple rules may cover a single instance, but predict different classes for it, thus requiring a conflict resolution strategy. In this work, we propose two algorithms capable of finding feature-space regions inside which any created rule would be consistent with the already existing rules, preventing inconsistencies from arising. Our algorithms do not generate classification models, but are instead meant to enhance algorithms that do so, such as Learning Classifier Systems. Both algorithms are described and analyzed exclusively from a theoretical perspective, since we have not modified a model-generating algorithm to incorporate our proposed solutions yet. This work presents the novelty of using conflict avoidance strategies instead of conflict resolution strategies.
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。
