FAME用模糊逻辑让模型既准确又易懂，规则少还清晰。
FAME: Introducing Fuzzy Additive Models for Explainable AI
- 用模糊逻辑构建可解释的加性模型，规则像自然语言。
- 只需少量活跃规则就精准捕捉输入输出关系。
- 适合需要透明决策的医疗、金融等高风险领域。
本文提出模糊加性模型（FAM）及其可解释扩展版FAME，用于可解释人工智能（XAI）。该模型包含三层：投影层压缩输入空间，模糊层基于单输入单输出模糊系统（SFLS），作为加性指数模型中的子网络，聚合层整合结果。此结构融合了SFLS的人类可读若-则规则与加性模型的可解释性，有效缓解维度灾难和规则爆炸问题。为进一步提升可解释性，提出对前提空间进行裁剪的方法，形成FAME。实验表明，FAME以更少活跃规则捕捉输入输出关系，显著提升清晰度。本文还构建了深度学习框架用于训练该模型族。对比结果证明，FAME在降低模型复杂度的同时保持强可解释性，是XAI领域的有力工具。
原文摘要 · Abstract (English)
In this study, we introduce the Fuzzy Additive Model (FAM) and FAM with Explainability (FAME) as a solution for Explainable Artificial Intelligence (XAI). The family consists of three layers: (1) a Projection Layer that compresses the input space, (2) a Fuzzy Layer built upon Single Input-Single Output Fuzzy Logic Systems (SFLS), where SFLS functions as subnetworks within an additive index model, and (3) an Aggregation Layer. This architecture integrates the interpretability of SFLS, which uses human-understandable if-then rules, with the explainability of input-output relationships, leveraging the additive model structure. Furthermore, using SFLS inherently addresses issues such as the curse of dimensionality and rule explosion. To further improve interpretability, we propose a method for sculpting antecedent space within FAM, transforming it into FAME. We show that FAME captures the input-output relationships with fewer active rules, thus improving clarity. To learn the FAM family, we present a deep learning framework. Through the presented comparative results, we demonstrate the promising potential of FAME in reducing model complexity while retaining interpretability, positioning it as a valuable tool for XAI.
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