A comparative study of six model complexity metrics to search for parsimonious models with GAparsimony R Package

Nowadays, interest is growing in automating KDD processes. Thanks to the increasing power and decreasing costs of computation devices, the search for the best features and model parameters can be conducted with different meta-heuristics. Thus, researchers can focus on other important tasks like data...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2021-09, Vol.452, p.317-332
Main Authors: Martinez-de-Pison, F.J., Ferreiro, J., Fraile, E., Pernia-Espinoza, A.
Format: Article
Language:English
Subjects:
Feature selection
GA-PARSIMONY
Genetic algorithms
Hyperparameter optimization
Parsimonious modeling
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Summary:Nowadays, interest is growing in automating KDD processes. Thanks to the increasing power and decreasing costs of computation devices, the search for the best features and model parameters can be conducted with different meta-heuristics. Thus, researchers can focus on other important tasks like data wrangling or feature engineering. This article details a comparative study of a GAparsimony R Package with six model complexity metrics. The objective was to identify an adequate model complexity measure for searching for accurate parsimonious solutions by combining feature selection, hyperparameter optimization, and parsimonious evaluation. This study also includes a regression code example to address some recommended precautions and considerations to find robust parsimonious models. This code can be easily adapted to other problems, databases, or algorithms.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2020.02.135