International Journal of Advanced Culture Technology

  • 제7권4호
  • /
  • Pages.56-62
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  • 2019
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  • 2288-7202(pISSN)
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  • 2288-7318(eISSN)
국제문화기술진흥원 (The International Promotion Agency of Culture Technology)
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Analysis of Open-Source Hyperparameter Optimization Software Trends

  • Lee, Yo-Seob (School of ICT Convergence, Pyeongtaek University) ;
  • Moon, Phil-Joo (Dept. of Information & Communication, Pyeongtaek University)
  • 투고 : 2019.09.26
  • 심사 : 2019.10.28
  • 발행 : 2019.12.31
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초록

Recently, research using artificial neural networks has further expanded the field of neural network optimization and automatic structuring from improving inference accuracy. The performance of the machine learning algorithm depends on how the hyperparameters are configured. Open-source hyperparameter optimization software can be an important step forward in improving the performance of machine learning algorithms. In this paper, we review open-source hyperparameter optimization softwares.

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참고문헌

  1. Yonghyuk Moon, Ikhee Shin, Yongju Shin, Okgi Min, "Recent Research & Development Trends in Automated Machine Learning," Electronics and Telecommunications Trends, Vol. 34 No. 4, pp. 32-42, Aug 1, 2019. https://dx.doi.org/10.22648/ETRI.2019.J.340404
  2. F. Hutter et al. (eds.), "Automated Machine Learning", The Springer Series on Challenges in Machine Learning, 2019, https://doi.org/10.1007/978-3-030-05318-5_1
  3. Hyperparameter Optimization, https://en.wikipedia.org/wiki/Hyperparameter_optimization.
  4. Claesen, Marc, and Bart De Moor. "Hyperparameter Search in Machine Learning", https://arxiv.org/abs/1502.02127
  5. Hyperparameter, https://en.wikipedia.org/wiki/Hyperparameter_(machine_learning).
  6. Scikit-learn, https://en.wikipedia.org/wiki/Scikit-learn.
  7. Scikit-learn, https://scikit-learn.org/.
  8. Tune, https://ray.readthedocs.io/en/latest/tune.html.
  9. Hyperopt, https://github.com/hyperopt/hyperopt.
  10. Auto-sklearn, https://github.com/automl/auto-sklearn.
  11. BOCS, https://github.com/baptistar/BOCS.
  12. mlrMBO, https://github.com/mlr-org/mlrMBO.
  13. scikit-optimize, https://github.com/scikit-optimize/scikit-optimize.
  14. FAR-HO, https://github.com/lucfra/FAR-HO.
  15. XGBoost, https://github.com/dmlc/xgboost.
  16. DEAP, https://github.com/DEAP/deap.
  17. DEvol, https://github.com/joeddav/devol.