Insights into Interpretability of Neuro-Fuzzy Systems
Marco Pota, Massimo Esposito
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.202How to use a DOI?
- Neuro-fuzzy systems, Semantic Interpretability, Complexity, Fuzzy sets shape, Rule weights.
Neuro-fuzzy networks revealed their proficiency in learning from data, while offering a transparent and somehow interpretable rule-based model. Recent research focused either on the interpretability of the chosen model or on the system performance. Regarding the interpretability, here an index to control the trade-off between complexity and performance, some insights into fuzzy partitions properties, an ideal fuzzy sets shape, and an evaluation of rules are proposed. All the evaluations are made taking into account the required output and performance. A discussion on results of a system built using the Wisconsin Breast Cancer Dataset is performed as a proof of concept.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Marco Pota AU - Massimo Esposito PY - 2015/06 DA - 2015/06 TI - Insights into Interpretability of Neuro-Fuzzy Systems BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1427 EP - 1434 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.202 DO - 10.2991/ifsa-eusflat-15.2015.202 ID - Pota2015/06 ER -