On fitting numerical features into probabilistic distributions to represent data for fuzzy pattern trees
Created by W.Langdon from
gp-bibliography.bib Revision:1.8576
- @Article{de-lima:2025:GPEM,
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author = "Allan {de Lima} and Juan F. H. Albarracin and
Douglas {Mota Dias} and Jorge Amaral and Conor Ryan",
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title = "On fitting numerical features into probabilistic
distributions to represent data for fuzzy pattern
trees",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2025",
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volume = "26",
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pages = "Article no: 25",
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note = "Online first",
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keywords = "genetic algorithms, genetic programming, Fuzzy pattern
trees, Bloat control, Lexicase selection",
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ISSN = "1389-2576",
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URL = "
https://rdcu.be/eC9IO",
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DOI = "
doi:10.1007/s10710-025-09522-9",
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size = "26 pages",
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abstract = "Fuzzy Pattern Trees (FPTs) are symbolic tree-based
structures whose internal nodes are fuzzy operators,
and the leaves are fuzzy features, which enhance
interpretability by representing data with meaningful
fuzzy terms. However, conventional FPT approaches
typically employ uniformly distributed membership
functions, which often fail to accurately represent
features in real-world datasets. we propose an
automatic method to adapt the bounds of fuzzy features
based on their data distributions, with a focus on a
simple triangular membership scheme. We evaluate our
approach across 11 benchmark classification problems,
incorporating six parsimony pressure methods to promote
more compact solutions. Our results demonstrate that
the adapted fuzzification scheme, beyond improving
interpretability, consistently yields models that
better balance accuracy and size when compared to
uniform representations, appearing on the Pareto front
20 times, while the second-best scheme appeared only 15
times.",
- }
Genetic Programming entries for
Allan Danilo de Lima
Juan Felipe Hernandez Albarracin
Douglas Mota Dias
Jorge Luis Machado Do Amaral
Conor Ryan
Citations