Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset

Amri, A’inur A’fifah and Ismail, Amelia Ritahani and Zarir, Abdullah Ahmad (2017) Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset. International Journal on Advanced Science, Engineering and Information Technology, 7 (6). pp. 2302-2307. ISSN 2088-5334 E-ISSN 2460-6952

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Abstract

Imbalanced class is one of the trials in classifying materials of big data. Data disparity produces a biased output of a model regardless how recent the technology is. However, deep learning algorithms such as convolutional neural networks and deep belief networks have proven to provide promising results in many research domains, especially in image processing as well as time series forecasting, intrusion detection, and classification. Therefore, this paper will investigate the effect of imbalanced data discrepancy of classes in MNIST handwritten dataset using convolutional neural networks and deep belief networks. Based on the experiment conducted, the results show that although the algorithm is suitable for multiple domains and have shown stability, the imbalanced distribution of data still able to affect the overall performance of the models.

Item Type:	Article (Journal)
Additional Information:	4296/62150
Uncontrolled Keywords:	Convolutional neural network; deep belief network; imbalanced class.
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:	22 Feb 2018 12:17
Last Modified:	19 Mar 2018 08:23
URI:	http://irep.iium.edu.my/id/eprint/62150

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