M6GP: Multiobjective Feature Engineering
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- @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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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 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 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