Dimensionality reduction using symbolic regression

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  author =       "Ilknur Icke and Andrew Rosenberg",
  title =        "Dimensionality reduction using symbolic regression",
  booktitle =    "GECCO 2010 Late breaking abstracts",
  year =         "2010",
  editor =       "Daniel Tauritz",
  isbn13 =       "978-1-4503-0073-5",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "2085--2086",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Portland, Oregon, USA",
  DOI =          "doi:10.1145/1830761.1830874",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "In this paper, we propose a symbolic regression
                 approach for data visualisation that is suited for
                 classification tasks. Our algorithm seeks a visually
                 and semantically interpretable lower dimensional
                 representation of the given dataset that would increase
                 classifier accuracy as well. This simultaneous
                 identification of easily interpretable dimensionality
                 reduction and improved classification accuracy relieves
                 the user of the burden of experimenting with the many
                 combinations of classification and dimensionality
                 reduction techniques",
  notes =        "Flubber, ECJ, WEKA, UCI wisconsin breast,
                 leptographsus crabs. Compare with PCA, MDS and random
                 projections. no significant improvement.

                 Also known as \cite{1830874} Distributed on CD-ROM at

                 ACM Order Number 910102.",

Genetic Programming entries for Ilknur Icke Andrew Rosenberg