Abstract
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.
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Acknowledgements
We are greatly indebted to Banco Santander for the APPI17/04 fellowship and to the University of La Rioja for the EGI16/19 fellowship. Also, A. Pernia wants to express her gratitude with the Instituto de Estudios Riojanos (IER) for the fellowship. This work used the Beronia cluster (Universidad de La Rioja), which is supported by FEDER-MINECO grant number UNLR-094E-2C-225.
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Martinez-de-Pison, F.J., Gonzalez-Sendino, R., Ferreiro, J., Fraile, E., Pernia-Espinoza, A. (2018). GAparsimony: An R Package for Searching Parsimonious Models by Combining Hyperparameter Optimization and Feature Selection. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_6
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