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All Journal International Journal of Advances in Intelligent Informatics
A’inur A’fifah Amri
Department of Computer Science, International Islamic University Malaysia
Computer Science & IT

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Evolutionary deep belief networks with bootstrap sampling for imbalanced class datasets A’inur A’fifah Amri; Amelia Ritahani Ismail; Omar Abdelaziz Mohammad
International Journal of Advances in Intelligent Informatics Vol 5, No 2 (2019): July 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v5i2.350

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.
Co-Authors Amelia Ritahani Ismail Omar Abdelaziz Mohammad