Hybrid methodology based on bayesian optimization and ga-parsimony for searching parsimony models by combining hyperparameter optimization and feature selection

  1. Martinez-De-pison, F.J. 1
  2. Gonzalez-Sendino, R. 1
  3. Aldama, A. 1
  4. Ferreiro, J. 1
  5. Fraile, E. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Zeitschrift:
Lecture Notes in Computer Science

ISSN: 0302-9743

Datum der Publikation: 2017

Ausgabe: 10334 LNCS

Seiten: 52-62

Art: Artikel

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DOI: 10.1007/978-3-319-59650-1_5 SCOPUS: 2-s2.0-85021721773 GOOGLE SCHOLAR

Andere Publikationen in: Lecture Notes in Computer Science

Zusammenfassung

This paper presents a hybrid methodology that combines Bayesian Optimization (BO) with a constrained version of the GA-PARSIMONY method to obtain parsimonious models. The proposal is designed to reduce the computational efforts associated to the use of GA-PARSIMONY alone. The method is initialized with BO to obtain favorable initial model parameters. With these parameters, a constrained GA-PARSIMONY is implemented to generate accurate parsimonious models using feature reduction, data transformation and parsimonious model selection. Finally, a second BO is run again with the selected features. Experiments with Extreme Gradient Boosting Machines (XGBoost) and six UCI databases demonstrate that the hybrid methodology obtains analogous models than the GA-PARSIMONY but with a significant reduction on the execution time in five of the six datasets. © Springer International Publishing AG 2017.