考虑特征约束的分类器解释新方法，避免冗余结果。
Abductive explanations of classifiers under constraints: Complexity and properties
- 基于解释覆盖范围定义新类型解释
- 可从全特征空间或数据集生成，减少冗余解释
- 提供不同复杂度与形式保证，适合需可靠解释的场景
反事实解释（AXp）广泛用于理解分类器决策。现有定义在特征独立时适用，但若忽略特征间的约束，可能导致冗余或多余解释数量爆炸。本文提出三种考虑约束的新解释类型，可从全特征空间或样本（如数据集）中生成。其核心是解释的覆盖范围——即该解释能说明的实例集合。覆盖范围足够强大，可剔除冗余和多余解释。针对每种类型，分析了寻找解释的复杂性并研究了其形式性质。最终形成一个包含不同形式、复杂度与形式保证的解释分类目录。
原文摘要 · Abstract (English)
Abductive explanations (AXp's) are widely used for understanding decisions of classifiers. Existing definitions are suitable when features are independent. However, we show that ignoring constraints when they exist between features may lead to an explosion in the number of redundant or superfluous AXp's. We propose three new types of explanations that take into account constraints and that can be generated from the whole feature space or from a sample (such as a dataset). They are based on a key notion of coverage of an explanation, the set of instances it explains. We show that coverage is powerful enough to discard redundant and superfluous AXp's. For each type, we analyse the complexity of finding an explanation and investigate its formal properties. The final result is a catalogue of different forms of AXp's with different complexities and different formal guarantees.
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