Interpretable Machine Learning: Explainability in Algorithm Design

Authors

  • Xueyi Cheng Duke University
  • Chang Che The George Washington University

DOI:

https://doi.org/10.70393/6a69656173.323337

ARK:

https://n2t.net/ark:/40704/JIEAS.v2n6a07

Disciplines:

Computer Science

Subjects:

Machine Learning

References:

22

Keywords:

Machine Learning, AutoML, Computational Efficiency

Abstract

In recent years, there is a high demand for transparency and accountability in machine learning models, especially in domains such as healthcare, finance and etc. In this paper, we delve into deep how to make machine learning models more interpretable, with focus on the importance of the explainability of the algorithm design. The main objective of this paper is to fill this gap and provide a comprehensive survey and analytical study towards AutoML. To that end, we first introduce the AutoML technology and review its various tools and techniques.

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Author Biographies

Xueyi Cheng, Duke University

Researcher at Duke University.

Chang Che, The George Washington University

The George Washington University, US.

References

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Hutter, F., Kotthoff, L., & Vanschoren, J. (2019). Automated machine learning: methods, systems, challenges (p. 219). Springer Nature.

Lin, Q., Che, C., Hu, H., Zhao, X., & Li, S. (2023). A Comprehensive Study on Early Alzheimer’s Disease Detection through Advanced Machine Learning Techniques on MRI Data. Academic Journal of Science and Technology, 8(1), 281-285.

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Che, C., & Tian, J. (2024). Understanding the Interrelation Between Temperature and Meteorological Factors: A Case Study of Szeged Using Machine Learning Techniques. Journal of Computer Technology and Applied Mathematics, 1(4), 47-52.

Cheng, X., Liu, K., Hu, X., Liu, T., Che, C., & Zhu, C. (2024). Comparative Analysis of Machine Learning Models for Music Recommendation. Theoretical and Natural Science, 53, 249-254.

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Published

2024-12-01

How to Cite

[1]
X. Cheng and C. Che, “Interpretable Machine Learning: Explainability in Algorithm Design”, Journal of Industrial Engineering & Applied Science, vol. 2, no. 6, pp. 65–70, Dec. 2024.

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