Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS

DOI

10.2991/eusflat-19.2019.67How to use a DOI?

Keywords

Fuzzy Rule Based Classification Systems Genetic Algorithm Multi-objective optimization

Abstract

Fuzzy Rule Based Classification Systems (FRBCSs) are widely used tools in classification problems. An important aspect in the design of a FRBCS is the number of fuzzy labels per variable (granularity level), which significantly influences the performance of the fuzzy system. Another relevant issue to be considered when generating a FRBCS is the accuracy-interpretability tradeoff, which can be addressed in the context of multi-objective optimization. Thus, in this work, we proposed a new approach to design a FRBCS in which the accuracy and the interpretability (number of rules) of the FRBCS are considered objectives to be treated with the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). We applied our method to several well-known standard classification datasets and the results show the feasibility of the proposed approach.

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  - Antonio Eloy de Oliveira Araujo
AU  - Renato Antonio Krohling
PY  - 2019/08
DA  - 2019/08
TI  - Generating a Fuzzy Rule Based Classification System by genetic learning of granularity level using TOPSIS
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 482
EP  - 489
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.67
DO  - 10.2991/eusflat-19.2019.67
ID  - deOliveiraAraujo2019/08
ER  -