Abstract
Fuzzy systems have demonstrated a strong modeling capability. The quality of a fuzzy model is usually measured in terms of its accuracy and interpretability. While the way to measure accuracy is in most cases clear, measuring interpretability is still an open question.
The use of hierarchical structures in fuzzy modeling as a way to reduce complexity in systems with many input variables has also shown good results. This complexity reduction is usually considered as a way to improve interpretability, but the real effect of the hierarchy on interpretability has not really been analyzed.
The present paper analyzes that complexity reduction comparing it with that of other techniques such as feature extraction, to conclude that only the use of intermediate variables with meaning (from the point of view of model interpretation) will ensure a real interpretability improvement due to the hierarchical structure.
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This paper was partially supported by Universidad Politécnica de Madrid (Spain).
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Magdalena, L. (2018). Do Hierarchical Fuzzy Systems Really Improve Interpretability?. In: Medina, J., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Foundations. IPMU 2018. Communications in Computer and Information Science, vol 853. Springer, Cham. https://doi.org/10.1007/978-3-319-91473-2_2
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