找出原型中真正关键的特征，让解释更聚焦有用信息。
This part looks alike this: identifying important parts of explained instances and prototypes
- 用无关解释方法计算特征重要性，找实例与原型的重合关键部分。
- 在六个数据集上提升理解度，且预测准确率不降反升。
- 适合想看清模型决策依据的研究者和工程师。
尽管基于原型的解释能以人类可理解的方式呈现模型预测，但往往未能引导用户关注最相关特征。本文提出一种新方法，识别原型中最具信息量的特征，称为‘ alike parts’。该方法利用无偏解释方法生成的特征重要性分数，突出实例与其最近原型之间最相关的重叠特征。同时，将特征重要性分数融入原型选择算法的目标函数，促进全局原型多样性。在六个基准数据集上的实验表明，该方法在保持或提升预测准确率的同时，显著改善了用户对模型决策的理解能力。
原文摘要 · Abstract (English)
Although prototype-based explanations provide a human-understandable way of representing model predictions they often fail to direct user attention to the most relevant features. We propose a novel approach to identify the most informative features within prototypes, termed alike parts. Using feature importance scores derived from an agnostic explanation method, it emphasizes the most relevant overlapping features between an instance and its nearest prototype. Furthermore, the feature importance score is incorporated into the objective function of the prototype selection algorithms to promote global prototypes diversity. Through experiments on six benchmark datasets, we demonstrate that the proposed approach improves user comprehension while maintaining or even increasing predictive accuracy.
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