A Graph Theory Approach to Fuzzy Rule Base Simplification

Caro E.M. Fuchs; Simone Spolaor; Marco S. Nobile; Uzay Kaymak

doi:10.1007/978-3-030-50146-4_29

A Graph Theory Approach to Fuzzy Rule Base Simplification

Caro E.M. Fuchs, Simone Spolaor, Marco S. Nobile, Uzay Kaymak

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

9 Citations (Scopus)

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.

Original language	English
Title of host publication	Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings
Editors	Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Anna Wilbik, Bernadette Bouchon-Meunier, Ronald R. Yager
Pages	387-401
Number of pages	15
ISBN (Electronic)	978-3-030-50146-4
DOIs	https://doi.org/10.1007/978-3-030-50146-4_29
Publication status	Published - 15 Jun 2020
Event	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020) - Lisboa, Portugal Duration: 15 Jun 2020 → 19 Jun 2020 https://ipmu2020.inesc-id.pt/

Publication series

Name	Communications in Computer and Information Science
Volume	1237 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020)
Country/Territory	Portugal
City	Lisboa
Period	15/06/20 → 19/06/20
Internet address	https://ipmu2020.inesc-id.pt/

Keywords

Data-driven modeling
Fuzzy logic
Graph theory
Open-source software
Python
Takagi–Sugeno fuzzy model

Access to Document

10.1007/978-3-030-50146-4_29

Cite this

Fuchs, C. E. M., Spolaor, S., Nobile, M. S., & Kaymak, U. (2020). A Graph Theory Approach to Fuzzy Rule Base Simplification. In M-J. Lesot, S. Vieira, M. Z. Reformat, J. P. Carvalho, A. Wilbik, B. Bouchon-Meunier, & R. R. Yager (Eds.), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings (pp. 387-401). (Communications in Computer and Information Science; Vol. 1237 CCIS). https://doi.org/10.1007/978-3-030-50146-4_29

Fuchs, Caro E.M. ; Spolaor, Simone ; Nobile, Marco S. et al. / A Graph Theory Approach to Fuzzy Rule Base Simplification. Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. editor / Marie-Jeanne Lesot ; Susana Vieira ; Marek Z. Reformat ; João Paulo Carvalho ; Anna Wilbik ; Bernadette Bouchon-Meunier ; Ronald R. Yager. 2020. pp. 387-401 (Communications in Computer and Information Science).

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title = "A Graph Theory Approach to Fuzzy Rule Base Simplification",

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.",

keywords = "Data-driven modeling, Fuzzy logic, Graph theory, Open-source software, Python, Takagi–Sugeno fuzzy model",

author = "Fuchs, {Caro E.M.} and Simone Spolaor and Nobile, {Marco S.} and Uzay Kaymak",

year = "2020",

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doi = "10.1007/978-3-030-50146-4_29",

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booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings",

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Fuchs, CEM , Spolaor, S , Nobile, MS & Kaymak, U 2020, A Graph Theory Approach to Fuzzy Rule Base Simplification. in M-J Lesot, S Vieira, MZ Reformat, JP Carvalho, A Wilbik, B Bouchon-Meunier & RR Yager (eds), Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. Communications in Computer and Information Science, vol. 1237 CCIS, pp. 387-401, 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020), Lisboa, Portugal, 15/06/20. https://doi.org/10.1007/978-3-030-50146-4_29

A Graph Theory Approach to Fuzzy Rule Base Simplification. / Fuchs, Caro E.M.; Spolaor, Simone ; Nobile, Marco S. et al.
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. ed. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Anna Wilbik; Bernadette Bouchon-Meunier; Ronald R. Yager. 2020. p. 387-401 (Communications in Computer and Information Science; Vol. 1237 CCIS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - A Graph Theory Approach to Fuzzy Rule Base Simplification

AU - Fuchs, Caro E.M.

AU - Spolaor, Simone

AU - Nobile, Marco S.

AU - Kaymak, Uzay

PY - 2020/6/15

Y1 - 2020/6/15

N2 - 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.

AB - 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.

KW - Data-driven modeling

KW - Fuzzy logic

KW - Graph theory

KW - Open-source software

KW - Python

KW - Takagi–Sugeno fuzzy model

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M3 - Conference contribution

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T3 - Communications in Computer and Information Science

SP - 387

EP - 401

BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings

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A2 - Vieira, Susana

A2 - Reformat, Marek Z.

A2 - Carvalho, João Paulo

A2 - Wilbik, Anna

A2 - Bouchon-Meunier, Bernadette

A2 - Yager, Ronald R.

T2 - 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020)

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ER -

Fuchs CEM , Spolaor S , Nobile MS , Kaymak U. A Graph Theory Approach to Fuzzy Rule Base Simplification. In Lesot M-J, Vieira S, Reformat MZ, Carvalho JP, Wilbik A, Bouchon-Meunier B, Yager RR, editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems - 18th International Conference, IPMU 2020, Proceedings. 2020. p. 387-401. (Communications in Computer and Information Science). doi: 10.1007/978-3-030-50146-4_29

A Graph Theory Approach to Fuzzy Rule Base Simplification

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