Performing multi-target regression via gene expression programming-based ensemble models
Created by W.Langdon from
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- @Article{journals/ijon/MoyanoPFV21,
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author = "Jose M. Moyano and Oscar Gabriel Reyes Pupo and
Habib M. Fardoun and Sebastian Ventura",
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title = "Performing multi-target regression via gene expression
programming-based ensemble models",
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journal = "Neurocomputing",
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year = "2021",
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volume = "432",
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pages = "275--287",
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keywords = "genetic algorithms, genetic programming, gene
expression programming, multi-target regression,
symbolic regression, ensemble-based model",
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ISSN = "0925-2312",
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bibdate = "2021-04-09",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/ijon/ijon432.html#MoyanoPFV21",
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URL = "https://www.sciencedirect.com/science/article/pii/S0925231220319603",
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DOI = "doi:10.1016/j.neucom.2020.12.060",
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abstract = "Multi-Target Regression problem comprises the
prediction of multiple continuous variables given a
common set of input features, unlike traditional
regression tasks, where just one output target is
available. There are two major challenges when
addressing this problem, namely the exploration of the
inter-target dependencies and the modelling of complex
input-output relationships. This work proposes a
Symbolic Regression method following the basis of Gene
Expression Programming paradigm to solve the
multi-target regression problem, and called GEPMTR. It
evolves a population of individuals, where each one
represents a complete solution to the problem by using
a multi-genic chromosome, and encodes a mathematical
function for each target variable involving the input
attributes. The proposed model can estimate the
inter-target dependencies by applying some genetic
operators. Furthermore, three ensemble-based methods
are developed to better exploit the inter-target and
input-output relationships. The effectiveness of the
proposals is analysed through an extensive experimental
study on 18 datasets. The codification schema and the
process followed to ensure a diverse population in
GEPMTR lead to obtain an effective proposal to solve
the MTR problem. Furthermore, the EGEPMTR-B ensemble
method obtained the best performance across all
proposed models, being the best in 8 out of 11 cases,
demonstrating that more sophisticated mechanisms were
not needed for ensuring that GEPMTR method would
properly model the existing inter-target dependencies.
Finally, the experimental results also showed that the
proposed approach attains competitive results compared
to state-of-the-art, showing the possibilities that can
bring this research line for effectively solving the
MTR problem.",
- }
Genetic Programming entries for
Jose M Moyano
Oscar Gabriel Reyes Pupo
Habib M Fardoun
Sebastian Ventura
Citations