Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Andrew Lensen and Bing Xue and Mengjie Zhang",
  title =        "Generating Redundant Features with Unsupervised
                 Multi-Tree Genetic Programming",
  booktitle =    "EuroGP 2018: Proceedings of the 21st European
                 Conference on Genetic Programming",
  year =         "2018",
  month =        "4-6 " # apr,
  editor =       "Mauro Castelli and Lukas Sekanina and 
                 Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
  series =       "LNCS",
  volume =       "10781",
  publisher =    "Springer Verlag",
  address =      "Parma, Italy",
  pages =        "84--100",
  organisation = "EvoStar, Species",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-77552-4",
  DOI =          "doi:10.1007/978-3-319-77553-1_6",
  abstract =     "Recently, feature selection has become an increasingly
                 important area of research due to the surge in
                 high-dimensional datasets in all areas of modern life.
                 A plethora of feature selection algorithms have been
                 proposed, but it is difficult to truly analyse the
                 quality of a given algorithm. Ideally, an algorithm
                 would be evaluated by measuring how well it removes
                 known bad features. Acquiring datasets with such
                 features is inherently difficult, and so a common
                 technique is to add synthetic bad features to an
                 existing dataset. While adding noisy features is an
                 easy task, it is very difficult to automatically add
                 complex, redundant features. This work proposes one of
                 the first approaches to generating redundant features,
                 using a novel genetic programming approach. Initial
                 experiments show that our proposed method can
                 automatically create difficult, redundant features
                 which have the potential to be used for creating
                 high-quality feature selection benchmark datasets.",
  notes =        "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in
                 conjunction with EvoCOP2018, EvoMusArt2018 and

Genetic Programming entries for Andrew Lensen Bing Xue Mengjie Zhang