Author(s):
Batista, João Eduardo ; Rodrigues, Nuno Miguel ; Vanneschi, Leonardo ; Silva, Sara
Date: 2024
Persistent ID: http://hdl.handle.net/10362/172920
Origin: Repositório Institucional da UNL
Project/scholarship:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT;
Subject(s): Genetic Programming; Multiobjective Optimization; Feature Engineering; Explainable AI; Interpretability; Artificial Intelligence; Computer Science Applications; Computer Vision and Pattern Recognition; Computational Mathematics; Control and Optimization
Description
Batista, J. E., Rodrigues, N. M., Vanneschi, L., & Silva, S. (2024). M6GP: Multiobjective Feature Engineering. In 2024 IEEE Congress on Evolutionary Computation (CEC) (pp. 1-8). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/CEC60901.2024.10612107 --- This work was supported by FCT through the LASIGE (UIDB/00408/20203 and UIDP/00408/20204) and MagIC/NOVA IMS (UIDB/04l52/2020) research units, and PhD Grant 202l/05322/BD
The current trend in machine learning is to use powerful algorithms to induce complex predictive models that often fall under the category of “black-box models”. Thanks to this, there is also a growing interest in studying model explainabil-ity and interpretability so that human experts can understand, validate, and correct those models. With the objective of promoting the creation of inherently interpretable models, we present M6GP. This wrapper-based multi-objective automatic feature engineering algorithm combines key components of the M3GP and NSGA-II algorithms. Wrapping M6GP around another machine learning algorithm evolves a set of features optimized for this algorithm while potentially increasing its robustness. We compare our results with M3GP and M4GP, two ancestors from the same algorithm family, and verify that, by using a multi-objective approach, M6GP obtains equal or better results. In addition, by using complexity metrics on the list of objectives, the M6GP models come down to one-fifth of the size of the M3GP models, making them easier to read by comparison.
