将模糊分类器转化为清晰规则分类器，提升可解释性。
Crisp complexity of fuzzy classifiers
- 提出模糊规则转清晰规则的系统方法。
- 分析转化后清晰分类器的复杂度特性。
- 帮助非模糊领域研究者理解模糊规则本质。
基于规则的系统是可解释人工智能中非常流行的形式，尤其在模糊领域，模糊规则广泛用于控制与分类问题。然而，模糊规则分类器难以在模糊领域外获得更大应用，部分原因在于用户对模糊概念不熟悉，且在某些情况下模糊划分不易理解。本文提出一种将模糊规则分类器简化为清晰规则分类器的方法，研究了多种可能的清晰描述方式，并实现相应算法以生成这些描述。同时，我们分析了所得清晰分类器的复杂度。结果表明，该方法有助于模糊与非模糊领域的研究者更好地理解模糊规则如何划分特征空间，以及两者之间的转换难易程度。此外，我们的复杂度度量可用于根据等效清晰划分的形态，在不同模糊分类器之间进行选择。
原文摘要 · Abstract (English)
Rule-based systems are a very popular form of explainable AI, particularly in the fuzzy community, where fuzzy rules are widely used for control and classification problems. However, fuzzy rule-based classifiers struggle to reach bigger traction outside of fuzzy venues, because users sometimes do not know about fuzzy and because fuzzy partitions are not so easy to interpret in some situations. In this work, we propose a methodology to reduce fuzzy rule-based classifiers to crisp rule-based classifiers. We study different possible crisp descriptions and implement an algorithm to obtain them. Also, we analyze the complexity of the resulting crisp classifiers. We believe that our results can help both fuzzy and non-fuzzy practitioners understand better the way in which fuzzy rule bases partition the feature space and how easily one system can be translated to another and vice versa. Our complexity metric can also help to choose between different fuzzy classifiers based on what the equivalent crisp partitions look like.
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