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RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm

Author

Listed:
  • Ángel Luis Muñoz Castañeda

    (Department of Mathematics, Universidad de León, 24007 León, Spain
    Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de León, 24007 León, Spain)

  • Noemí DeCastro-García

    (Department of Mathematics, Universidad de León, 24007 León, Spain
    Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de León, 24007 León, Spain)

  • David Escudero García

    (Research Institute of Applied Sciences in Cybersecurity (RIASC), Universidad de León, 24007 León, Spain)

Abstract

This work proposes a new algorithm for optimizing hyper-parameters of a machine learning algorithm, RHOASo, based on conditional optimization of concave asymptotic functions. A comparative analysis of the algorithm is presented, giving particular emphasis to two important properties: the capability of the algorithm to work efficiently with a small part of a dataset and to finish the tuning process automatically, that is, without making explicit, by the user, the number of iterations that the algorithm must perform. Statistical analyses over 16 public benchmark datasets comparing the performance of seven hyper-parameter optimization algorithms with RHOASo were carried out. The efficiency of RHOASo presents the positive statistically significant differences concerning the other hyper-parameter optimization algorithms considered in the experiments. Furthermore, it is shown that, on average, the algorithm needs around 70 % of the iterations needed by other algorithms to achieve competitive performance. The results show that the algorithm presents significant stability regarding the size of the used dataset partition.

Suggested Citation

  • Ángel Luis Muñoz Castañeda & Noemí DeCastro-García & David Escudero García, 2021. "RHOASo: An Early Stop Hyper-Parameter Optimization Algorithm," Mathematics, MDPI, vol. 9(18), pages 1-52, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2334-:d:639562
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    References listed on IDEAS

    as
    1. P. Baldi & P. Sadowski & D. Whiteson, 2014. "Searching for exotic particles in high-energy physics with deep learning," Nature Communications, Nature, vol. 5(1), pages 1-9, September.
    2. Noemí DeCastro-García & Ángel Luis Muñoz Castañeda & David Escudero García & Miguel V. Carriegos, 2019. "Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm," Complexity, Hindawi, vol. 2019, pages 1-16, February.
    3. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
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