Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO
, Corrado Mencar- DOI
- 10.2991/ijcis.d.200904.002How to use a DOI?
- Keywords
- Strong fuzzy partition; Trapezoidal fuzzy sets; Fuzzy rule-based classifier; Defuzzification; Particle swarm optimization
- Abstract
We study the influence of fuzziness of trapezoidal fuzzy sets in the strong fuzzy partitions (SFPs) that constitute the database of a fuzzy rule-based classifier. To this end, we develop a particular representation of the trapezoidal fuzzy sets that is based on the concept of cuts, which are the cross-points of fuzzy sets in a SFP and fix the position of the fuzzy sets in the Universe of Discourse. In this way, it is possible to isolate the parameters that characterize the fuzziness of the fuzzy sets, which are subject to fine-tuning through particle swarm optimization (PSO). In this paper, we propose a formulation of the parameter space that enables the exploration of all possible levels of fuzziness in a SFP. The experimental results show that the impact of fuzziness is strongly dependent on the defuzzification procedure used in fuzzy rule-based classifiers. Fuzziness has little influence in the case of winner-takes-all defuzzification, while it is more influential in weighted sum defuzzification, which however may pose some interpretation problems.
- Copyright
- © 2020 The Authors. Published by Atlantis Press B.V.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Ciro Castiello AU - Corrado Mencar PY - 2020 DA - 2020/09/17 TI - Fine-Tuning the Fuzziness of Strong Fuzzy Partitions through PSO JO - International Journal of Computational Intelligence Systems SP - 1415 EP - 1428 VL - 13 IS - 1 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.200904.002 DO - 10.2991/ijcis.d.200904.002 ID - Castiello2020 ER -