Automatically evolving difficult benchmark feature selection datasets with genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Lensen:2018:GECCO,
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author = "Andrew Lensen and Bing Xue and Mengjie Zhang",
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title = "Automatically evolving difficult benchmark feature
selection datasets with genetic programming",
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booktitle = "GECCO '18: Proceedings of the Genetic and Evolutionary
Computation Conference",
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year = "2018",
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editor = "Hernan Aguirre and Keiki Takadama and
Hisashi Handa and Arnaud Liefooghe and Tomohiro Yoshikawa and
Andrew M. Sutton and Satoshi Ono and Francisco Chicano and
Shinichi Shirakawa and Zdenek Vasicek and
Roderich Gross and Andries Engelbrecht and Emma Hart and
Sebastian Risi and Ekart Aniko and Julian Togelius and
Sebastien Verel and Christian Blum and Will Browne and
Yusuke Nojima and Tea Tusar and Qingfu Zhang and
Nikolaus Hansen and Jose Antonio Lozano and
Dirk Thierens and Tian-Li Yu and Juergen Branke and
Yaochu Jin and Sara Silva and Hitoshi Iba and
Anna I Esparcia-Alcazar and Thomas Bartz-Beielstein and
Federica Sarro and Giuliano Antoniol and Anne Auger and
Per Kristian Lehre",
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isbn13 = "978-1-4503-5618-3",
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pages = "458--465",
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address = "Kyoto, Japan",
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DOI = "doi:10.1145/3205455.3205552",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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keywords = "genetic algorithms, genetic programming",
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abstract = "There has been a wealth of feature selection
algorithms proposed in recent years, each of which
claims superior performance in turn. A wide range of
datasets have been used to compare these algorithms,
each with different characteristics and quantities of
redundant and noisy features. Hence, it is very
difficult to comprehensively and fairly compare these
feature selection methods in order to find which are
most robust and effective. In this work, we examine
using Genetic Programming to automatically synthesise
redundant features for augmenting existing datasets in
order to more scientifically test feature selection
performance. We develop a method for producing complex
multi-variate redundancies, and present a novel and
intuitive approach to ensuring a range of redundancy
relationships are automatically created. The
application of these augmented datasets to
well-established feature selection algorithms shows a
number of interesting and useful results and suggests
promising directions for future research in this
area.",
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notes = "Also known as \cite{3205552} GECCO-2018 A
Recombination of the 27th International Conference on
Genetic Algorithms (ICGA-2018) and the 23rd Annual
Genetic Programming Conference (GP-2018)",
- }
Genetic Programming entries for
Andrew Lensen
Bing Xue
Mengjie Zhang
Citations