Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data

Amri, A’inur A’fifah and Ismail, Amelia Ritahani and Zarir, Abdullah Ahmad (2018) Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data. International Journal of Advanced Computer Science and Applications, 9 (2). pp. 258-264. ISSN 2156-5570 E-ISSN 2158-107X

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Abstract

Imbalanced data is one of the challenges in a classification task in machine learning. Data disparity produces a biased output of a model regardless how recent the technology is. However, deep learning algorithms, such as deep belief networks showed promising results in many domains, especially in image processing. Therefore, in this paper, we will review the effect of imbalanced data disparity in classes using deep belief networks as the benchmark model and compare it with conventional machine learning algorithms, such as backpropagation neural networks, decision trees, naïve Bayes and support vector machine with MNIST handwritten dataset. The experiment shows that although the algorithm is stable and suitable for multiple domains, the imbalanced data distribution still manages to affect the outcome of the conventional machine learning algorithms.

Item Type:	Article (Journal)
Additional Information:	4296/62456
Uncontrolled Keywords:	Deep belief networks; support vector machine; back propagation neural networks; imbalanced handwritten data; classification
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:	26 Mar 2018 14:30
Last Modified:	02 Jan 2024 10:43
URI:	http://irep.iium.edu.my/id/eprint/62456

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