EnsembleForge: A Comprehensive Framework for Simplified Training and Deployment of Stacked Ensemble Models in Classification Tasks

  • Ubong David Essien Akwa Ibom State University, Nigeria
  • Godwin Okon Ansa Akwa Ibom State University, Nigeria
  • Aloysius Akpnobong Akwa Ibom State University, Nigeria
Keywords: Machine Learning, Stacking Technique, Ensemble Model, Classification

Abstract

In this work, we introduce EnsembleForge, a versatile framework designed to streamline machine learning experimentation and simplify classification tasks. Leveraging the stacking ensemble method, EnsembleForge offers an intuitive platform built upon the Scikit-learn library. This framework facilitates seamless model implementation and evaluation, supporting both Randomized and Grid Search for hyperparameter optimization. Our experiments with publicly available datasets demonstrate the ease of use and effectiveness of EnsembleForge in experimenting with various algorithms. With its adaptability and innovation, EnsembleForge showcases promising potential to serve as an asset for researchers and practitioners seeking to achieve optimal model performance in their machine learning endeavors.

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Author Biography

Godwin Okon Ansa, Akwa Ibom State University

Associate Professor
Department Of Computer Science,Akwa Ibom State University

References

K. M. R. Alam, N. Siddique, and H. Adeli, "A dynamic ensemble learning algorithm for neural networks," Neural Computing and Applications, vol. 32, pp. 8675-8690, 2020.

D. B. Araya, K. Grolinger, H. F. ElYamany, M. A. Capretz, and G. Bitsuamlak, "An ensemble learning framework for anomaly detection in building energy consumption," Energy and Buildings, vol. 144, pp. 191-206, 2017.

A. Chatzimparmpas, R. M. Martins, K. Kucher, and A. Kerren, "StackGenVis: Alignment of data, algorithms, and models for stacking ensemble learning using performance metrics," IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1547-1557, 2020.

V. Chaurasia and S. Pal, "An ensemble framework-stacking and feature selection technique for detection of breast cancer," International Journal of Medical Engineering and Informatics, vol. 14, no. 3, pp. 240-251, 2022.

Y. Chen, Y. Zhou, M. Park, S. Tran, S. Hadley, and Q. Bai, "A novel adaptive ensemble learning framework for automated Beggiatoa Spp. coverage estimation," Expert Systems with Applications, vol. 237, p. 121416, 2023.

J. Divakarr, "Phone Classification Dataset," Kaggle, Retrieved January 9, 2024, from https://www.kaggle.com/datasets/jacksondivakarr/phone-classification-dataset.

A. Mohammed and R. Kora, "A comprehensive review on ensemble deep learning: Opportunities and challenges," Journal of King Saud University-Computer and Information Sciences, 2023.

A. Ă–zdemir, U. Yavuz, and F. A. Dael, "Performance evaluation of different classification techniques using different datasets," International Journal of Electrical & Computer Engineering, vol. 9, no. 5, 2019.

F. Pedregosa et al., "Scikit-learn: Machine learning in Python," the Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.

P. Pintelas and I. E. Livieris, "Special issue on ensemble learning and applications," Algorithms, vol. 13, no. 6, p. 140, 2020.

L. Ren, H. Zhang, A. S. Seklouli, T. Wang, and A. Bouras, "Stacking-based multi-objective ensemble framework for prediction of hypertension," Expert Systems with Applications, vol. 215, p. 119351, 2023.

A. K. Seewald, "Towards a theoretical framework for ensemble classification," in IJCAI, vol. 3, pp. 1443-1444, 2003.

N. Singh and P. Singh, "Stacking-based multi-objective evolutionary ensemble framework for prediction of diabetes mellitus," Biocybernetics and Biomedical Engineering, vol. 40, no. 1, pp. 1-22, 2020.

W. Wolberg, "Breast Cancer Wisconsin (Original) [Data file]," UCI Machine Learning Repository, https://doi.org/10.24432/C5HP4Z, 1992.

Y. Zhang, J. Liu, and W. Shen, "A review of ensemble learning algorithms used in remote sensing applications," Applied Sciences, vol. 12, no. 17, p. 8654, 2022.

Published
2024-03-23
Abstract views: 105 times
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How to Cite
Essien, U., Ansa, G., & Akpnobong, A. (2024). EnsembleForge: A Comprehensive Framework for Simplified Training and Deployment of Stacked Ensemble Models in Classification Tasks. Journal of Information Systems and Informatics, 6(1), 68-82. https://doi.org/10.51519/journalisi.v6i1.643