A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems
Authors
Adriano S. Koshiyama, Marley M.B.R. Vellasco, Ricardo Tanscheit
Corresponding Author
Adriano S. Koshiyama
Available Online June 2015.
- DOI
- 10.2991/ifsa-eusflat-15.2015.105How to use a DOI?
- Keywords
- Genetic fuzzy system, genetic programming, regression.
- Abstract
This work presents a novel Genetic Fuzzy System (GFS), called Genetic Programming Fuzzy Inference System for Regression problems (GPFISRegress). It makes use of Multi-Gene Genetic Programming to build the premises of fuzzy rules, including t-norms, negation and linguistic hedge operators. GPFIS-Regress also defines a consequent term that is more compatible with a given premise and makes use of aggregation operators to weigh fuzzy rules in accordance with their influence on the problem. The system has been applied to a set of benchmarks and has also been compared to other GFSs, showing competitive results in terms of accuracy and interpretability.
- 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/).
Cite this article
TY - CONF AU - Adriano S. Koshiyama AU - Marley M.B.R. Vellasco AU - Ricardo Tanscheit PY - 2015/06 DA - 2015/06 TI - A Multi-gene Genetic Programming Fuzzy Inference System for Regression Problems 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 - 742 EP - 748 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.105 DO - 10.2991/ifsa-eusflat-15.2015.105 ID - Koshiyama2015/06 ER -