GAparsimony: An R package for searching parsimonious models by combining hyperparameter optimization and feature selection

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

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revista:
Lecture Notes in Computer Science

ISSN: 0302-9743

Año de publicación: 2018

Volumen: 10870 LNAI

Páginas: 62-73

Tipo: Artículo

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DOI: 10.1007/978-3-319-92639-1_6 SCOPUS: 2-s2.0-85048877422 GOOGLE SCHOLAR

Otras publicaciones en: Lecture Notes in Computer Science

Resumen

Nowadays, there is an increasing interest in automating KDD processes. Thanks to the increasing power and costs reduction of computation devices, the search of best features and model parameters can be solved with different meta-heuristics. Thus, researchers can be focused in other important tasks like data wrangling or feature engineering. In this contribution, GAparsimony R package is presented. This library implements GA-PARSIMONY methodology that has been published in previous journals and HAIS conferences. The objective of this paper is to show how to use GAparsimony for searching accurate parsimonious models by combining feature selection, hyperparameter optimization, and parsimonious model search. Therefore, this paper covers the cautions and considerations required for finding a robust parsimonious model by using this package and with a regression example that can be easily adapted for another problem, database or algorithm. © Springer International Publishing AG, part of Springer Nature 2018.