Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

International Journal of Advances in Intelligent Informatics

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Title Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
Creator Amri, A’inur A’fifah
Ismail, Amelia Ritahani
Mohammad, Omar Abdelaziz
Description 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 this paper, the genetic algorithm (GA) and bootstrap sampling are incorporated into DBN to lessen the drawbacks occurs when imbalanced class datasets are used. The performance of the proposed algorithm is compared with DBN and is evaluated using performance metrics. The results showed that there is an improvement in performance when Evolutionary DBN with bootstrap sampling is used to handle imbalanced class datasets.
Publisher Universitas Ahmad Dahlan
Date 2019-07-26
Type info:eu-repo/semantics/article

Format application/pdf
Identifier http://ijain.org/index.php/IJAIN/article/view/350
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 2 (2019): July 2019; 123-136
Language eng
Relation http://ijain.org/index.php/IJAIN/article/view/350/ijain_v5i2_p123-136
Rights https://creativecommons.org/licenses/by-sa/4.0

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