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DOI: 10.14569/IJACSA.2018.090236
PDF

Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data

Author 1: A’inur A’fifah Amri
Author 2: Amelia Ritahani Ismail
Author 3: Abdullah Ahmad Zarir

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 9 Issue 2, 2018.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

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.

Keywords: Deep belief networks; support vector machine; back propagation neural networks; imbalanced handwritten data; classification

A’inur A’fifah Amri, Amelia Ritahani Ismail and Abdullah Ahmad Zarir, “Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data” International Journal of Advanced Computer Science and Applications(IJACSA), 9(2), 2018. http://dx.doi.org/10.14569/IJACSA.2018.090236

@article{Amri2018,
title = {Comparative Performance of Deep Learning and Machine Learning Algorithms on Imbalanced Handwritten Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2018.090236},
url = {http://dx.doi.org/10.14569/IJACSA.2018.090236},
year = {2018},
publisher = {The Science and Information Organization},
volume = {9},
number = {2},
author = {A’inur A’fifah Amri and Amelia Ritahani Ismail and Abdullah Ahmad Zarir}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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