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Effect of the Sampling of a Dataset in the Hyperparameter Optimization Phase over the Efficiency of a Machine Learning Algorithm

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  • Noemí DeCastro-García
  • Ángel Luis Muñoz Castañeda
  • David Escudero García
  • Miguel V. Carriegos

Abstract

Selecting the best configuration of hyperparameter values for a Machine Learning model yields directly in the performance of the model on the dataset. It is a laborious task that usually requires deep knowledge of the hyperparameter optimizations methods and the Machine Learning algorithms. Although there exist several automatic optimization techniques, these usually take significant resources, increasing the dynamic complexity in order to obtain a great accuracy. Since one of the most critical aspects in this computational consume is the available dataset, among others, in this paper we perform a study of the effect of using different partitions of a dataset in the hyperparameter optimization phase over the efficiency of a Machine Learning algorithm. Nonparametric inference has been used to measure the rate of different behaviors of the accuracy, time, and spatial complexity that are obtained among the partitions and the whole dataset. Also, a level of gain is assigned to each partition allowing us to study patterns and allocate whose samples are more profitable. Since Cybersecurity is a discipline in which the efficiency of Artificial Intelligence techniques is a key aspect in order to extract actionable knowledge, the statistical analyses have been carried out over five Cybersecurity datasets.

Suggested Citation

  • 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.
  • Handle: RePEc:hin:complx:6278908
    DOI: 10.1155/2019/6278908
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    References listed on IDEAS

    as
    1. Zhun Cheng & Zhixiong Lu, 2018. "A Novel Efficient Feature Dimensionality Reduction Method and Its Application in Engineering," Complexity, Hindawi, vol. 2018, pages 1-14, October.
    2. Massimiliano Zanin & Miguel Romance & Santiago Moral & Regino Criado, 2018. "Credit Card Fraud Detection through Parenclitic Network Analysis," Complexity, Hindawi, vol. 2018, pages 1-9, May.
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    Cited by:

    1. Yuchen Wang & Zhengshan Luo & Jihao Luo & Yiqiong Gao & Yulei Kong & Qingqing Wang, 2023. "Investigation of the Solubility of Elemental Sulfur (S) in Sulfur-Containing Natural Gas with Machine Learning Methods," IJERPH, MDPI, vol. 20(6), pages 1-21, March.
    2. 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.

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