Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts

DOI

10.2991/eusflat-19.2019.60How to use a DOI?

Keywords

Strong fuzzy partitions Particle Swarm Optimization Trapezoidal fuzzy sets DC*

Abstract

Cut-based strong fuzzy partitions (SFP) are characterized by cuts, i.e. points in the universe of discourse where the non-zero membership degrees of the fuzzy sets in the partition is 0.5. Cuts are useful to identify the most representative regions for the fuzzy sets involved in a SFP but pose loose constraints on the slopes of trapezoidal fuzzy sets. We address the problem of optimizing such slopes in order to maximize the performance of fuzzy rule-based systems while keeping cuts constant. This way, model performance is improved and interpretability is preserved. We use Particle Swarm Optimization to perform optimization and we analyze two different approaches for generating solution spaces. We tested the proposed approach on a number of fuzzy rule-based classifiers designed by DC* on synthetic data. For all the considered models, performance is never degraded but improved in many cases, without violating any interpretability constraint.

Copyright

© 2019, 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  - Ciro Castiello
AU  - Corrado Mencar
PY  - 2019/08
DA  - 2019/08
TI  - Exploiting Particle Swarm Optimization to Attune Strong Fuzzy Partitions Based on Cuts
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 430
EP  - 437
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.60
DO  - 10.2991/eusflat-19.2019.60
ID  - Castiello2019/08
ER  -