Decision Trees based Fuzzy Rules

DOI

10.2991/itsmssm-16.2016.91How to use a DOI?

Keywords

Decision tree, Fuzzy logic, Forecasting, Classification, Inductive learning.

Abstract

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.

Copyright

© 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/).

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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  - 10.2991/itsmssm-16.2016.91
ID  - Al-Gunaid2016/05
ER  -