Designing fuzzy inference systems from data: An interpretability-oriented review

By Serge Guillaume. 2001

778 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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Citations in the corpus

What do we need to build explainable AI systems for the medical domain? (2017, background): “The latter have a long tradition and can be designed from expert knowledge or from data and provides - from the viewpoint of HCI - a good framework for the interaction between human expert knowledge and hidden knowledge in the data (Guillaume, 2001).”
Interpretable Decision Sets: A Joint Framework for Description and Prediction (2016, background): “Many applications require that models be interpretable, meaning that humans can easily understand the information they contain [8, 13, 32, 35].”
Interpretability of linguistic fuzzy rule-based systems: An overview of interpretability measures (2011, methodology): “After a preliminary review of interpretability-oriented fuzzy inference systems obtained from data presented by Guillaume in [29], Guillaume and Charnomordic in [30] present a method for generating interpretable fuzzy rules from data.”
FURIA: an algorithm for unordered fuzzy rule induction (2009, background): “They are not very flexible and suffer from the “curse of dimensionality” in the case of many input variables but may have advantages with respect to interpretability (Guillaume 2001).”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, background): “Thus the main strength of NFSs is that they are universal approximators [28]–[30] with the ability to solicit interpretable IF–THEN rules [31].”
Low-level interpretability and high-level interpretability: a unified view of data-driven interpretable fuzzy system modelling (2008, methodology): “However, the above question was not considered and addressed in [67].”
Causability and explainability of artificial intelligence in medicine (2019, background): “The latter have a long tradition and can be designed from expert knowledge or from data and provides — from the viewpoint of Human-AI interaction — a good framework for the interaction between human expert knowledge and hidden knowledge in the data (Guillaume, 2001).”
Real-time system for monitoring driver vigilance (2004, methodology): “A lot of methods are available in the fuzzy literature [29].”
A historical review of evolutionary learning methods for Mamdani-type fuzzy rule-based systems: Designing interpretable genetic fuzzy systems (2011, positive): “In the last few years, it has been shown an increasing interest on considering fuzzy techniques to design both accurate and interpretable fuzzy models [1,34,37,81,118,148,155].”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “by representing each antecedent and consequent as fuzzy sets [43].”