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

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

Dettagli Bibliografici
Parent link:	Advances in Computer Science Research Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 502-508]
Ente Autore:	Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Altri autori:	Mohammed Al-Gunaid, Shcherbakov M. Maxim, Kamaev V. Valeriy, Gerget O. M. Olga Mikhailovna, Tyukov A. P. Anton
Riassunto:	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.
Lingua:	inglese
Pubblicazione:	2016
Soggetti:	электронный ресурс труды учёных ТПУ decision tree fuzzy logic forecasting classification inductive learning нечеткая логика прогнозирование классификация обучение решения
Accesso online:	http://dx.doi.org/10.2991/itsmssm-16.2016.91
Natura:	MixedMaterials Elettronico Capitolo di libro
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657509

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