M6GP: Multiobjective Feature Engineering
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gp-bibliography.bib Revision:1.8120
- @InProceedings{batista:2024:CEC,
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author = "Joao Eduardo Batista and Nuno Miguel Rodrigues and
Leonardo Vanneschi and Sara Silva",
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title = "{M6GP:} Multiobjective Feature Engineering",
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booktitle = "2024 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2024",
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editor = "Bing Xue",
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address = "Yokohama, Japan",
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month = "30 " # jun # " - 5 " # jul,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, Measurement,
Training, Machine learning algorithms, Power demand,
Machine learning, Predictive models, Market research,
Multiobjective Optimization, Feature Engineering,
Explainable AI, Interpretability",
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isbn13 = "979-8-3503-0837-2",
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URL = "http://hdl.handle.net/10362/172920",
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DOI = "doi:10.1109/CEC60901.2024.10612107",
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abstract = "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 explainability 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 optimised 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.",
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notes = "also known as \cite{10612107}
WCCI 2024",
- }
Genetic Programming entries for
Joao Eduardo Batista
Nuno Miguel Rodrigues Domingos
Leonardo Vanneschi
Sara Silva
Citations