Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks

Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks

Imbalanced class is one of the challenges in classifying 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 showed promising results in many domains, es...

Full description

Bibliographic Details
Main Authors: Amri, A’inur A’fifah, Ismail, Amelia Ritahani, Abdullah, Ahmad Zarir
Format: Conference or Workshop Item
Language:English
Published: 2016
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/53436/
http://irep.iium.edu.my/53436/
http://irep.iium.edu.my/53436/9/53436.pdf
id iium-53436
recordtype eprints
spelling iium-534362016-12-21T01:00:16Z http://irep.iium.edu.my/53436/ Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks Amri, A’inur A’fifah Ismail, Amelia Ritahani Abdullah, Ahmad Zarir QA75 Electronic computers. Computer science Imbalanced class is one of the challenges in classifying 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 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 convolutional neural networks and deep belief networks as the benchmark model, and a modified MNIST handwritten dataset as the bench- mark 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 model. 2016-08 Conference or Workshop Item NonPeerReviewed application/pdf en http://irep.iium.edu.my/53436/9/53436.pdf Amri, A’inur A’fifah and Ismail, Amelia Ritahani and Abdullah, Ahmad Zarir (2016) Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks. In: The 3rd International Multi-Conference on Artificial Intelligence Technology (M-CAIT 2016), 23rd-24th August 2016, Malacca, Malaysia. (Unpublished) http://www.ftsm.ukm.my/mcait2016/
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Amri, A’inur A’fifah
Ismail, Amelia Ritahani
Abdullah, Ahmad Zarir
Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
description Imbalanced class is one of the challenges in classifying 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 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 convolutional neural networks and deep belief networks as the benchmark model, and a modified MNIST handwritten dataset as the bench- mark 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 model.
format Conference or Workshop Item
author Amri, A’inur A’fifah
Ismail, Amelia Ritahani
Abdullah, Ahmad Zarir
author_facet Amri, A’inur A’fifah
Ismail, Amelia Ritahani
Abdullah, Ahmad Zarir
author_sort Amri, A’inur A’fifah
title Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
title_short Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
title_full Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
title_fullStr Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
title_full_unstemmed Exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
title_sort exploring imbalanced class issue in handwritten dataset using convolutional neural networks and deep belief networks
publishDate 2016
url http://irep.iium.edu.my/53436/
http://irep.iium.edu.my/53436/
http://irep.iium.edu.my/53436/9/53436.pdf
first_indexed 2023-09-18T21:15:36Z
last_indexed 2023-09-18T21:15:36Z
_version_ 1777411540967751680

Similar Items