Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine

Decision Trees based Fuzzy Rules

Authors
Mohammed Al-Gunaid, Maxim Shcherbakov, Valeriy Kamaev, Olga Gerget, Anton Tyukov
Corresponding Author
Mohammed Al-Gunaid
Available Online May 2016.
DOI
https://doi.org/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.
Open Access
This is an open access article distributed under the CC BY-NC license.

Download article (PDF)

Proceedings
Information Technologies in Science, Management, Social Sphere and Medicine
Part of series
Advances in Computer Science Research
Publication Date
May 2016
ISBN
978-94-6252-196-4
ISSN
2352-538X
DOI
https://doi.org/10.2991/itsmssm-16.2016.91How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

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  - Information Technologies in Science, Management, Social Sphere and Medicine
PB  - Atlantis Press
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  -