Decision Trees based Fuzzy Rules
- https://doi.org/10.2991/itsmssm-16.2016.91How to use a DOI?
- Decision tree, Fuzzy logic, Forecasting, Classification, Inductive learning.
Decision trees have been recognized as interpretable, efficient, problem independent and scalable architectures. In case of fuzzy representation there is no procedure of automation tree building. In other words existing approaches of building decision trees and fuzzy decision trees cannot provide automatically generate fuzzy sets and fuzzy knowledge bases to build fuzzy decision trees. Paper presents a new method of building fuzzy decision trees called decision trees based fuzzy rules (DTFR). This method combines tree growing and pruning, to determine the structure of the FDT, to improve its generalization capabilities. Proposes a method (DTFR) considered as a variant of decision tree inductive using fuzzy set theory.
- © 2016, 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 - Mohammed Al-Gunaid AU - Maxim Shcherbakov AU - Valeriy Kamaev AU - Olga Gerget AU - Anton Tyukov PY - 2016/05 DA - 2016/05 TI - Decision Trees based Fuzzy Rules BT - Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine PB - Atlantis Press SP - 463 EP - 470 SN - 2352-538X UR - https://doi.org/10.2991/itsmssm-16.2016.91 DO - https://doi.org/10.2991/itsmssm-16.2016.91 ID - Al-Gunaid2016/05 ER -