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)

Détails bibliographiques
Parent link:Advances in Computer Science Research
Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 502-508]
Collectivité auteur: Национальный исследовательский Томский политехнический университет Инженерная школа информационных технологий и робототехники Отделение информационных технологий
Autres auteurs: Mohammed Al-Gunaid, Shcherbakov M. Maxim, Kamaev V. Valeriy, Gerget O. M. Olga Mikhailovna, Tyukov A. P. Anton
Résumé: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.
Langue:anglais
Publié: 2016
Sujets:
электронный ресурс
труды учёных ТПУ
decision tree
fuzzy logic
forecasting
classification
inductive learning
нечеткая логика
прогнозирование
классификация
обучение
решения
Accès en ligne:http://dx.doi.org/10.2991/itsmssm-16.2016.91
Format: Électronique Chapitre de livre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657509
Description
Résumé: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.
DOI:10.2991/itsmssm-16.2016.91

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