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...

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Main Authors:	Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Mohammad, Omar Abdelaziz
Format:	Article
Language:	English English
Published:	2019
Subjects:	QA75 Electronic computers. Computer science
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

id	iium-77295
recordtype	eprints
spelling	iium-772952020-03-02T06:53:39Z http://irep.iium.edu.my/77295/ Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets Amri, A’inur A’fifah Ismail, Amelia Ritahani Mohammad, Omar Abdelaziz QA75 Electronic computers. Computer science 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. 2019-07 Article PeerReviewed application/pdf en http://irep.iium.edu.my/77295/1/Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets.pdf application/pdf en http://irep.iium.edu.my/77295/7/77295_Evolutionary%20deep%20belief%20networks%20with%20bootstrap%20sampling%20for%20imbalanced%20class%20datasets_Scopus.pdf Amri, A’inur A’fifah and Ismail, Amelia Ritahani and Mohammad, Omar Abdelaziz (2019) Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets. International Journal of Advances in Intelligent Informatics, 5 (2). pp. 123-136. ISSN 2442-6571
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Amri, A’inur A’fifah Ismail, Amelia Ritahani Mohammad, Omar Abdelaziz Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
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.
format	Article
author	Amri, A’inur A’fifah Ismail, Amelia Ritahani Mohammad, Omar Abdelaziz
author_facet	Amri, A’inur A’fifah Ismail, Amelia Ritahani Mohammad, Omar Abdelaziz
author_sort	Amri, A’inur A’fifah
title	Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
title_short	Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
title_full	Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
title_fullStr	Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
title_full_unstemmed	Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
title_sort	evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets
publishDate	2019
url	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
first_indexed	2023-09-18T21:49:02Z
last_indexed	2023-09-18T21:49:02Z
_version_	1777413644175278080

Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

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