Genetic Programming-based Construction of Features for Machine Learning and Knowledge Discovery Tasks
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gp-bibliography.bib Revision:1.8051
- @Article{krawiec:2002:GPEM,
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author = "Krzysztof Krawiec",
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title = "Genetic Programming-based Construction of Features for
Machine Learning and Knowledge Discovery Tasks",
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journal = "Genetic Programming and Evolvable Machines",
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year = "2002",
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volume = "3",
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number = "4",
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pages = "329--343",
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month = dec,
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keywords = "genetic algorithms, genetic programming, machine
learning, change of representation, feature
construction, feature selection",
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ISSN = "1389-2576",
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DOI = "doi:10.1023/A:1020984725014",
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abstract = "we use genetic programming for changing the
representation of the input data for machine learners.
In particular, the topic of interest here is feature
construction in the learning-from-examples paradigm,
where new features are built based on the original set
of attributes. The paper first introduces the general
framework for GP-based feature construction. Then, an
extended approach is proposed where the useful
components of representation (features) are preserved
during an evolutionary run, as opposed to the standard
approach where valuable features are often lost during
search. Finally, we present and discuss the results of
an extensive computational experiment carried out on
several reference data sets. The outcomes show that
classifiers induced using the representation enriched
by the GP-constructed features provide better accuracy
of classification on the test set. In particular, the
extended approach proposed in the paper proved to be
able to outperform the standard approach on some
benchmark problems on a statistically significant
level.",
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notes = "Article ID: 5103872",
- }
Genetic Programming entries for
Krzysztof Krawiec
Citations