Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
Imbalanced class data is a common issue faced in classification tasks. Deep Belief Networks (DBN) is a promising deep learning algorithm when learning from complex feature input. However, when handling imbalanced class data, DBN encounters low performance as other machine learning algorithms. In thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/77295/ http://irep.iium.edu.my/77295/1/Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets.pdf http://irep.iium.edu.my/77295/7/77295_Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets_Scopus.pdf |
Internet
http://irep.iium.edu.my/77295/http://irep.iium.edu.my/77295/1/Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets.pdf
http://irep.iium.edu.my/77295/7/77295_Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets_Scopus.pdf