Automatically evolving difficult benchmark feature selection datasets with genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Lensen:2018:GECCO,
  author =       "Andrew Lensen and Bing Xue and Mengjie Zhang",
  title =        "Automatically evolving difficult benchmark feature
                 selection datasets with genetic programming",
  booktitle =    "GECCO '18: Proceedings of the Genetic and Evolutionary
                 Computation Conference",
  year =         "2018",
  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",
  isbn13 =       "978-1-4503-5618-3",
  pages =        "458--465",
  address =      "Kyoto, Japan",
  DOI =          "doi:10.1145/3205455.3205552",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        "15-19 " # jul,
  organisation = "SIGEVO",
  keywords =     "genetic algorithms, genetic programming",
  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.",
  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