AI 中文总结
研究决策树中无关条件删除问题，基于二元分裂使类比例反向移动的结构事实，提出结构IRC删除框架，通过评估预测可靠性诊断相关性，选择性删除无关条件，实现规则显著简化且不牺牲原树可靠性。
AI 中文摘要
决策树生成可解释的if-then规则，但包含无关条件（IRC）。这些IRC源于树分裂的结构机制，即使在现代最优稀疏树归纳算法中也存在。现有IRC删除方法忽略此结构机制，要么保留原树太宽松不可靠，要么太严格无法实现有意义的简化。本研究通过建立与IRC机制相关的定理和命题，为可靠的IRC删除提供理论基础。关键发现是二元分裂会使类比例相对于父节点向相反方向移动，从而产生C1链接和C0链接。基于此，提出结构IRC删除框架，通过评估预测可靠性严格诊断其相关性，选择性删除结构和经验上无关的条件，同时保护那些删除会降低规则可靠性的条件。实验结果证实该框架在不牺牲原树可靠性的情况下实现了显著的规则简化。
英文摘要
Decision trees generate interpretable if--then rules, yet they contain irrelevant conditions (IRCs). These IRCs arise from the structural mechanism of tree splitting and persist even in modern optimal sparse tree induction algorithms. Existing IRC deletion methods overlook this structural mechanism; therefore, they either preserve the original tree too loosely to remain reliable, or too strictly to achieve meaningful simplification. This study provides theoretical foundations for reliable IRC deletion by establishing theorems and propositions related to the underlying IRC mechanism. The key finding is that a binary split shifts class proportions in opposite directions relative to the parent. Specifically, an increase in the class-1 proportion along one branch necessitates an increase in the class-0 proportion along its sibling, thereby generating a C1-link and a C0-link. Based on this structural fact, we propose a structural IRC deletion framework. Relative to each leaf, links that increase the leaf-class proportion are matched, whereas links that increase the proportion of the opposite leaf-class are mismatched. These mismatched links are flagged as structurally suspicious IRC candidates. Rather than deleting them outright, the framework rigorously diagnoses their relevance by assessing prediction reliability. It selectively deletes conditions that are structurally and empirically irrelevant, while strictly protecting those whose deletion would reduce the rule's reliability. Experimental results confirm that the proposed framework achieves substantial rule simplification without sacrificing the reliability of the original tree.
Comments31 pages, 6 figures
