A binary-constrained Geometric Semantic Genetic Programming for feature selection purposes
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gp-bibliography.bib Revision:1.7964
- @Article{journals/prl/PapaRP17,
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author = "Joao Paulo Papa and Gustavo Henrique Rosa and
Luciene Patrici Papa",
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title = "A binary-constrained Geometric Semantic Genetic
Programming for feature selection purposes",
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journal = "Pattern Recognition Letters",
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year = "2017",
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volume = "100",
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pages = "59--66",
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month = "1 " # dec,
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keywords = "genetic algorithms, genetic programming, feature
selection, geometric semantic genetic programming,
optimum-path forest",
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ISSN = "0167-8655",
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URL = "http://www.sciencedirect.com/science/article/pii/S0167865517303641",
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DOI = "doi:10.1016/j.patrec.2017.10.002",
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bibdate = "2017-11-29",
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bibsource = "DBLP,
http://dblp.uni-trier.de/db/journals/prl/prl100.html#PapaRP17",
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abstract = "Feature selection concerns the task of finding the
subset of features that are most relevant to some
specific problem in the context of machine learning. By
selecting proper features, one can reduce the
computational complexity of the learnt model, and to
possibly enhance its effectiveness by reducing the
well-known overfitting. During the last years, the
problem of feature selection has been modelled as an
optimisation task, where the idea is to find the subset
of features that maximise some fitness function, which
can be a given classifier's accuracy or even some
measure concerning the samples' separability in the
feature space, for instance. In this paper, we
introduced Geometric Semantic Genetic Programming
(GSGP) in the context of feature selection, and we
experimentally showed it can work properly with both
conic and non-conic fitness landscapes. We observed
that there is no need to restrict the feature selection
modelling into GSGP constraints, which can be quite
useful to adopt the semantic operators to a broader
range of applications.",
-
notes = "Also known as \cite{PAPA201759}",
- }
Genetic Programming entries for
Joao Paulo Papa
Gustavo Rosa
Luciene Patrici Papa
Citations