Decision Trees based Fuzzy Rules; Advances in Computer Science Research; Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016)

Detalles Bibliográficos
Parent link:Advances in Computer Science Research
Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 502-508]
Autor Corporativo: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники
Outros autores: 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.
Idioma:inglés
Publicado: 2016
Subjects:
Acceso en liña:http://dx.doi.org/10.2991/itsmssm-16.2016.91
Formato: Electrónico Capítulo de libro
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657509