Insights into Interpretability of Neuro-Fuzzy Systems

DOI

10.2991/ifsa-eusflat-15.2015.202How to use a DOI?

Keywords

Neuro-fuzzy systems, Semantic Interpretability, Complexity, Fuzzy sets shape, Rule weights.

Abstract

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.

Copyright

© 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/).

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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  -