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

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...

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Main Authors:	Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Zarir, Abdullah Ahmad
Format:	Article
Language:	English English English
Published:	The Science and Information (SAI) Organization 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://irep.iium.edu.my/62456/ http://irep.iium.edu.my/62456/ http://irep.iium.edu.my/62456/7/62456-Comparative%20performance%20of%20deep%20learning%20_scopus.pdf http://irep.iium.edu.my/62456/8/62456-Comparative%20Performance%20of%20Deep%20Learning_%20article.pdf http://irep.iium.edu.my/62456/19/62456_Comparative%20performance%20of%20deep%20learning_WOS.pdf

id	iium-62456
recordtype	eprints
spelling	iium-624562018-09-12T04:20:42Z http://irep.iium.edu.my/62456/ Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data Amri, A’inur A’fifah Ismail, Amelia Ritahani Zarir, Abdullah Ahmad QA75 Electronic computers. Computer science 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. The Science and Information (SAI) Organization 2018-02 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62456/7/62456-Comparative%20performance%20of%20deep%20learning%20_scopus.pdf application/pdf en http://irep.iium.edu.my/62456/8/62456-Comparative%20Performance%20of%20Deep%20Learning_%20article.pdf application/pdf en http://irep.iium.edu.my/62456/19/62456_Comparative%20performance%20of%20deep%20learning_WOS.pdf 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 http://thesai.org/Downloads/Volume9No2/Paper_36-Comparative_Performance_of_Deep_Learning.pdf
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Amri, A’inur A’fifah Ismail, Amelia Ritahani Zarir, Abdullah Ahmad Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
description	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.
format	Article
author	Amri, A’inur A’fifah Ismail, Amelia Ritahani Zarir, Abdullah Ahmad
author_facet	Amri, A’inur A’fifah Ismail, Amelia Ritahani Zarir, Abdullah Ahmad
author_sort	Amri, A’inur A’fifah
title	Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
title_short	Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
title_full	Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
title_fullStr	Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
title_full_unstemmed	Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
title_sort	comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
publisher	The Science and Information (SAI) Organization
publishDate	2018
url	http://irep.iium.edu.my/62456/ http://irep.iium.edu.my/62456/ http://irep.iium.edu.my/62456/7/62456-Comparative%20performance%20of%20deep%20learning%20_scopus.pdf http://irep.iium.edu.my/62456/8/62456-Comparative%20Performance%20of%20Deep%20Learning_%20article.pdf http://irep.iium.edu.my/62456/19/62456_Comparative%20performance%20of%20deep%20learning_WOS.pdf
first_indexed	2023-09-18T21:28:30Z
last_indexed	2023-09-18T21:28:30Z
_version_	1777412352838205440

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

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