Feature Selection to Improve Generalisation of Genetic Programming for High-Dimensional Symbolic Regression
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- @Article{Chen:2017:ieeeTEC,
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author = "Qi Chen and Mengjie Zhang and Bing Xue",
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title = "Feature Selection to Improve Generalisation of Genetic
Programming for High-Dimensional Symbolic Regression",
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journal = "IEEE Transactions on Evolutionary Computation",
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year = "2017",
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volume = "21",
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number = "5",
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pages = "792--806",
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month = oct,
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keywords = "genetic algorithms, genetic programming, Symbolic
Regression",
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ISSN = "1089-778X",
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DOI = "doi:10.1109/TEVC.2017.2683489",
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abstract = "When learning from high-dimensional data for symbolic
regression, genetic programming typically could not
generalise well. Feature selection, as a data
preprocessing method, can potentially contribute not
only to improving the efficiency of learning algorithms
but also to enhancing the generalisation ability.
However, in genetic programming for high-dimensional
symbolic regression, feature selection before learning
is seldom considered. In this work, we propose a new
feature selection method based on permutation to select
features for high dimensional symbolic regression using
genetic programming. A set of experiments has been
conducted to investigate the performance of the
proposed method on the generalisation of genetic
programming for high-dimensional symbolic regression.
The regression results confirm the superior performance
of the proposed method over the other examined feature
selection methods. Further analysis indicates that the
models evolved by the proposed method are more likely
to contain only the truly relevant features and have
better interpretability.",
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notes = "also known as \cite{7879832}",
- }
Genetic Programming entries for
Qi Chen
Mengjie Zhang
Bing Xue
Citations