Sensitivity-like Analysis for Feature Selection in Genetic Programming
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- @InProceedings{Dick:2017:GECCO,
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author = "Grant Dick",
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title = "Sensitivity-like Analysis for Feature Selection in
Genetic Programming",
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booktitle = "Proceedings of the Genetic and Evolutionary
Computation Conference",
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series = "GECCO '17",
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year = "2017",
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isbn13 = "978-1-4503-4920-8",
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address = "Berlin, Germany",
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pages = "401--408",
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size = "8 pages",
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URL = "http://doi.acm.org/10.1145/3071178.3071338",
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DOI = "doi:10.1145/3071178.3071338",
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acmid = "3071338",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, CART, feature
selection, random forests, symbolic regression,
variable importance",
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month = "15-19 " # jul,
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abstract = "feature selection is an important process within
machine learning problems. Through pressures imposed on
models during evolution, genetic programming performs
basic feature selection, and so analysis of the evolved
models can provide some insights into the utility of
input features. Previous work has tended towards a
presence model of feature selection, where the
frequency of a feature appearing within evolved models
is a metric for its utility. In this paper, we identify
some drawbacks with using this approach, and instead
propose the integration of importance measures for
feature selection that measure the influence of a
feature within a model. Using sensitivity-like analysis
methods inspired by importance measures used in random
forest regression, we demonstrate that genetic
programming introduces many features into evolved
models that have little impact on a given model's
behaviour, and this can mask the true importance of
salient features. The paper concludes by exploring
bloat control methods and adaptive terminal selection
methods to influence the identification of useful
features within the search performed by genetic
programming, with results suggesting that a combination
of adaptive terminal selection and bloat control may
help to improve generalisation performance.",
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notes = "Also known as \cite{Dick:2017:SAF:3071178.3071338}
GECCO-2017 A Recombination of the 26th International
Conference on Genetic Algorithms (ICGA-2017) and the
22nd Annual Genetic Programming Conference (GP-2017)",
- }
Genetic Programming entries for
Grant Dick
Citations