ExplainabilityTransparencyAccountabilitycs.AI
Crisp complexity of fuzzy classifiers
Raquel Fernandez-Peralta, Javier Fumanal-Idocin, Javier Andreu-Perez
April 22, 2025
IEEE International Conference on Fuzzy Systems
1 citations
Key Takeaways
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Fuzzy rule-based classifiers can be converted into simpler, crisp (non-fuzzy) rule systems that may be easier for non-experts to understand and adopt, potentially making explainable AI more accessible beyond specialized fuzzy logic communities.
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The paper provides a complexity metric for evaluating fuzzy classifiers by examining what their equivalent crisp rule partitions look like, offering practitioners a new tool to compare and select between different fuzzy classifiers based on interpretability rather than just accuracy.
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Fuzzy partitions can be difficult to interpret in real-world applications, which has limited the adoption of fuzzy rule-based systems outside academic venues—this work addresses that barrier by showing how to translate fuzzy systems into more transparent crisp alternatives.
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The methodology reveals how fuzzy rule bases partition feature space, helping both fuzzy and non-fuzzy practitioners understand the underlying logic of these systems and making it easier to translate between fuzzy and crisp approaches depending on use case needs.
Abstract
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.