Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

International Journal of Advances in Intelligent Informatics

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Field Value
 
Title Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
 
Creator Amri, A’inur A’fifah
Ismail, Amelia Ritahani
Mohammad, Omar Abdelaziz
 
Subject
 
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
 
Contributor
 
Date 2019-07-26
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion

 
Format application/pdf
 
Identifier http://ijain.org/index.php/IJAIN/article/view/350
10.26555/ijain.v5i2.350
 
Source International Journal of Advances in Intelligent Informatics; Vol 5, No 2 (2019): July 2019; 123-136
2548-3161
2442-6571
 
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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