Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making

DOI

10.2991/ifsa-eusflat-15.2015.79How to use a DOI?

Keywords

Membership function, decision tree, fuzzy rule, histogram analysis, entropy.

Abstract

Nowadays, decision making using fuzzy logic is a ma-jor research area for scientists, researchers and project managers. Construction of membership functions and fuzzy rules from numerical data is very important in various applications of the fuzzy set theory. Therefore, in this paper a model is proposed for development of membership functions and fuzzy rules from numerical data for decision making. The main advantage of the proposed model is its simplicity. The proposed model is applied on Fisher’s Iris data for decision making. The validation result shows that proposed model has a higher accuracy than existing models.

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/).

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TY  - CONF
AU  - Dilip Kumar Yadav
AU  - Harikesh Bahadur Yadav
PY  - 2015/06
DA  - 2015/06
TI  - Developing Membership Functions and Fuzzy Rules from Numerical Data for Decision Making
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  - 551
EP  - 555
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.79
DO  - 10.2991/ifsa-eusflat-15.2015.79
ID  - Yadav2015/06
ER  -