Decision Trees based Fuzzy Rules; Advances in Computer Science Research; Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016)
| Parent link: | Advances in Computer Science Research Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 502-508] |
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| مؤلف مشترك: | |
| مؤلفون آخرون: | , , , , |
| الملخص: | 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. |
| اللغة: | الإنجليزية |
| منشور في: |
2016
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://dx.doi.org/10.2991/itsmssm-16.2016.91 |
| التنسيق: | الكتروني فصل الكتاب |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=657509 |
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| 200 | 1 | |a Decision Trees based Fuzzy Rules |f Mohammed Al-Gunaid [et al.] | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: p. 507-508 (27 tit.)] | ||
| 330 | |a 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. | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\18167 |t Advances in Computer Science Research | |
| 463 | 0 | |0 (RuTPU)RU\TPU\network\18169 |t Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016) |o III International Scientific Conference, May 23-26, 2016, Tomsk, Russia |o [proceedings] |f National Research Tomsk Polytechnic University (TPU) ; eds. O. G. Berestneva, A. Tikhomirov, A. Trufanov |v [P. 502-508] |d 2016 | |
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a decision tree | |
| 610 | 1 | |a fuzzy logic | |
| 610 | 1 | |a forecasting | |
| 610 | 1 | |a classification | |
| 610 | 1 | |a inductive learning | |
| 610 | 1 | |a нечеткая логика | |
| 610 | 1 | |a прогнозирование | |
| 610 | 1 | |a классификация | |
| 610 | 1 | |a обучение | |
| 610 | 1 | |a решения | |
| 701 | 1 | |a Mohammed Al-Gunaid | |
| 701 | 1 | |a Shcherbakov |b M. |g Maxim | |
| 701 | 1 | |a Kamaev |b V. |g Valeriy | |
| 701 | 1 | |a Gerget |b O. M. |c Specialist in the field of informatics and computer technology |c Professor of Tomsk Polytechnic University, Doctor of Sciences |f 1974- |g Olga Mikhailovna |3 (RuTPU)RU\TPU\pers\31430 |9 15593 | |
| 701 | 1 | |a Tyukov |b A. P. |g Anton | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа информационных технологий и робототехники |b Отделение информационных технологий |3 (RuTPU)RU\TPU\col\23515 |
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| 856 | 4 | |u http://dx.doi.org/10.2991/itsmssm-16.2016.91 | |
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