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...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/53436/ http://irep.iium.edu.my/53436/ http://irep.iium.edu.my/53436/9/53436.pdf |
| Summary: | 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. |
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