dc.date.accessioned |
2021-06-04T08:06:08Z |
|
dc.date.available |
2021-06-04T08:06:08Z |
|
dc.date.issued |
2021 |
|
dc.identifier.issn |
1064-1246 |
|
dc.identifier.uri |
https://hdl.handle.net/20.500.12619/95640 |
|
dc.description |
Bu yayının lisans anlaşması koşulları tam metin açık erişimine izin vermemektedir. |
|
dc.description.abstract |
Genetic algorithm is one of data mining classification techniques and it has been applied successfully in a wide range of applications. However, the performance of Genetic algorithm fluctuates significantly. This research work combines Genetic algorithm with fuzzy logic to adapt dynamically crossover and mutation parameters of Genetic algorithm. Two different datasets are taken during the experiment. Several experiments have been performed to prove the effectiveness of the proposed algorithm. Results show that the rules generated from a proposed algorithm are significantly better with high fitness and more efficient as compared to a normal Genetic algorithm. |
|
dc.language |
English |
|
dc.language |
İngilizce |
|
dc.language.iso |
eng |
|
dc.publisher |
IOS PRESS |
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dc.rights |
info:eu-repo/semantics/closedAccess |
|
dc.title |
Hybrid fuzzy-genetic algorithm to automated discovery of prediction rules |
|
dc.type |
Article |
|
dc.identifier.volume |
40 |
|
dc.identifier.startpage |
43 |
|
dc.identifier.endpage |
52 |
|
dc.relation.journal |
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS |
|
dc.identifier.issue |
1 |
|
dc.identifier.wos |
WOS:000606807200004 |
|
dc.identifier.doi |
10.3233/JIFS-182729 |
|
dc.identifier.eissn |
1875-8967 |
|
dc.contributor.author |
Fadel, Ibrahim A. |
|
dc.contributor.author |
Alsanabani, Hussein |
|
dc.contributor.author |
Oz, Cemil |
|
dc.contributor.author |
Kamal, Tariq |
|
dc.contributor.author |
Iskefiyeli, Murat |
|
dc.contributor.author |
Abdien, Fawzia |
|
dc.relation.publicationcategory |
Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı |
|