Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively investigating methods to design interpretable FIS. Following this line of research, we propose a new approach for FIS simplification which leverages graph theory to identify and remove similar fuzzy sets from rule bases. We test our methodology on two data sets to show how this approach can be used to simplify the rule base without sacrificing accuracy.

Fuchs, C., Spolaor, S., Nobile, M., Kaymak, U. (2020). A Graph Theory Approach to Fuzzy Rule Base Simplification. In 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 (pp.387-401). Springer [10.1007/978-3-030-50146-4_29].

A Graph Theory Approach to Fuzzy Rule Base Simplification

Spolaor S.;Nobile M. S.;
2020

Abstract

Fuzzy inference systems (FIS) gained popularity and found application in several fields of science over the last years, because they are more transparent and interpretable than other common (black-box) machine learning approaches. However, transparency is not automatically achieved when FIS are estimated from data, thus researchers are actively investigating methods to design interpretable FIS. Following this line of research, we propose a new approach for FIS simplification which leverages graph theory to identify and remove similar fuzzy sets from rule bases. We test our methodology on two data sets to show how this approach can be used to simplify the rule base without sacrificing accuracy.
paper
Data-driven modeling; Fuzzy logic; Graph theory; Open-source software; Python; Takagi–Sugeno fuzzy model;
English
18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
2020
Lesot M.-J.,Vieira S.,Reformat M.Z.,Carvalho J.P.,Wilbik A.,Bouchon-Meunier B.,Yager R.R.
18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020
9783030501457
2020
1237
387
401
none
Fuchs, C., Spolaor, S., Nobile, M., Kaymak, U. (2020). A Graph Theory Approach to Fuzzy Rule Base Simplification. In 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020 (pp.387-401). Springer [10.1007/978-3-030-50146-4_29].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298366
Citazioni
  • Scopus 14
  • ???jsp.display-item.citation.isi??? ND
Social impact