提出更贴近决策的稀疏解释方法，让解释更简洁有用。
Improving Decision Sparsity
- 基于超立方体路径定义决策稀疏性，动态调整参考点增强解释灵活性。
- 新方法在多种函数类上实现更稀疏且有意义的解释，提升可解释性。
- 适合关注模型决策透明度的研究者和实践者使用。
稀疏性是机器学习可解释性的核心。传统稀疏性通常以模型全局变量数量衡量，但对决策过程并无直接意义——被决策者关心的只是影响结果的变量。本文大幅拓展了决策稀疏性指标「稀疏解释值（SEV）」，使其解释更贴合实际。SEV通过在超立方体中沿路径向参考点移动来建模，并允许灵活设定参考点，同时考虑超立方体距离与特征空间距离的映射关系，从而为各类函数类生成更稀疏、更可信的解释。本文提出基于聚类的SEV与基于树的SEV变体，引入提升解释可信度的方法，并设计优化决策稀疏性的算法。
原文摘要 · Abstract (English)
Sparsity is a central aspect of interpretability in machine learning. Typically, sparsity is measured in terms of the size of a model globally, such as the number of variables it uses. However, this notion of sparsity is not particularly relevant for decision-making; someone subjected to a decision does not care about variables that do not contribute to the decision. In this work, we dramatically expand a notion of decision sparsity called the Sparse Explanation Value(SEV) so that its explanations are more meaningful. SEV considers movement along a hypercube towards a reference point. By allowing flexibility in that reference and by considering how distances along the hypercube translate to distances in feature space, we can derive sparser and more meaningful explanations for various types of function classes. We present cluster-based SEV and its variant tree-based SEV, introduce a method that improves credibility of explanations, and propose algorithms that optimize decision sparsity in machine learning models.
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