Decision Trees based Fuzzy Rules

Decision Trees based Fuzzy Rules

Bibliographic Details
Parent link:Advances in Computer Science Research
Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 502-508]
Corporate Author: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Other Authors: Mohammed Al-Gunaid, Shcherbakov M. Maxim, Kamaev V. Valeriy, Gerget O. M. Olga Mikhailovna, Tyukov A. P. Anton
Summary:Title screen
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.
Published: 2016
Subjects:
электронный ресурс
труды учёных ТПУ
decision tree
fuzzy logic
forecasting
classification
inductive learning
нечеткая логика
прогнозирование
классификация
обучение
решения
Online Access:http://dx.doi.org/10.2991/itsmssm-16.2016.91
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657509

Internet

http://dx.doi.org/10.2991/itsmssm-16.2016.91

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