Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets

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

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Abstract

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.

Item Type:	Article (Journal)
Additional Information:	4296/77295
Uncontrolled Keywords:	Imbalanced class, Deep belief networks, Genetic algorithm, Bootstrapping sampling, Complex feature input
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	08 Jan 2020 16:15
Last Modified:	02 Jan 2024 10:19
URI:	http://irep.iium.edu.my/id/eprint/77295

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