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Research Article | Open Access |

Enhancing Question Pairs Identification with Ensemble Learning: Integrating Machine Learning and Deep Learning Models

Author 1: Salsabil Tarek Author 2: Hatem M. Noaman Author 3: Mohammed Kayed
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 14, No. 11 · Published 2023 · Cited by 5

DOI: https://doi.org/10.14569/IJACSA.2023.01411100

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Abstract

The effectiveness of machine learning (ML) and deep learning (DL) models on the Quora question pairs dataset is investigated in this study. ML models, including AdaBoost, reached 73.44% test accuracy, while ensemble learning approaches enhanced outcomes even further, with the Hard-Voting Ensemble achieving 76.13%. DL models, such as FCN, demonstrated test accuracy of 81% with cross validation. These findings contribute to natural language processing by demonstrating the potential of ensemble learning for ML models and the DL models' detailed pattern-capturing capacity.

Keywords

Ensemble learning natural language processing deep learning machine learning

How to Cite this Article

Tarek, S., Noaman, H. M., & Kayed, M. (2023). Enhancing Question Pairs Identification with Ensemble Learning: Integrating Machine Learning and Deep Learning Models. International Journal of Advanced Computer Science and Applications, 14(11). https://doi.org/10.14569/IJACSA.2023.01411100

Tarek, Salsabil, et al.. "Enhancing Question Pairs Identification with Ensemble Learning: Integrating Machine Learning and Deep Learning Models." International Journal of Advanced Computer Science and Applications, vol. 14, no. 11, 2023, https://doi.org/10.14569/IJACSA.2023.01411100.

@article{Tarek2023,
  title     = {Enhancing Question Pairs Identification with Ensemble Learning: Integrating Machine Learning and Deep Learning Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Salsabil Tarek and Hatem M. Noaman and Mohammed Kayed},
  doi       = {10.14569/IJACSA.2023.01411100},
  url       = {https://doi.org/10.14569/IJACSA.2023.01411100}
}

Open Access — licensed under a Creative Commons Attribution 4.0 International License. Unrestricted use, distribution, and reproduction in any medium, even commercially, as long as the original work is properly cited.

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