Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset
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...
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INSIGHT - Indonesian Society for Knowledge and Human Development
2017
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iium-621502018-03-19T00:23:16Z http://irep.iium.edu.my/62150/ Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset Amri, A’inur A’fifah Ismail, Amelia Ritahani Zarir, Abdullah Ahmad QA75 Electronic computers. Computer science 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. INSIGHT - Indonesian Society for Knowledge and Human Development 2017-12-31 Article PeerReviewed application/pdf en http://irep.iium.edu.my/62150/1/Convolutional%20Neural%20Networks%20and%20Deep%20Belief%20Networks.pdf application/pdf en http://irep.iium.edu.my/62150/7/62150_Convolutional%20Neural%20Networks%20and%20Deep%20Belief%20Networks_scopus.pdf 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 http://www.insightsociety.org/ojaseit/index.php/ijaseit/article/view/2632/pdf_614 |
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Digital Repository |
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Local University |
| institution |
International Islamic University Malaysia |
| building |
IIUM Repository |
| collection |
Online Access |
| language |
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 Convolutional neural networks and deep belief networks for analysing imbalanced class issue in handwritten dataset |
| description |
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. |
| 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 |
Convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| title_short |
Convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| title_full |
Convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| title_fullStr |
Convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| title_full_unstemmed |
Convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| title_sort |
convolutional neural networks and deep belief networks
for analysing imbalanced class issue in handwritten dataset |
| publisher |
INSIGHT - Indonesian Society for Knowledge and Human Development |
| publishDate |
2017 |
| url |
http://irep.iium.edu.my/62150/ http://irep.iium.edu.my/62150/ http://irep.iium.edu.my/62150/1/Convolutional%20Neural%20Networks%20and%20Deep%20Belief%20Networks.pdf http://irep.iium.edu.my/62150/7/62150_Convolutional%20Neural%20Networks%20and%20Deep%20Belief%20Networks_scopus.pdf |
| first_indexed |
2023-09-18T21:28:07Z |
| last_indexed |
2023-09-18T21:28:07Z |
| _version_ |
1777412328551088128 |