Domain-independent feature extraction for multi-classification using multi-objective genetic programming

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  author =       "Yang Zhang and Peter Rockett",
  title =        "Domain-independent feature extraction for
                 multi-classification using multi-objective genetic
  journal =      "Pattern Analysis and Applications",
  year =         "2010",
  number =       "3",
  volume =       "13",
  pages =        "273--288",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1433-7541",
  DOI =          "doi:10.1007/s10044-009-0154-1",
  size =         "16 pages",
  abstract =     "We propose three model-free feature extraction
                 approaches for solving the multiple class
                 classification problem; we use multi-objective genetic
                 programming (MOGP) to derive (near-)optimal feature
                 extraction stages as a precursor to classification with
                 a simple and fast-to-train classifier.
                 Statistically-founded comparisons are made between our
                 three proposed approaches and seven conventional
                 classifiers over seven datasets from the UCI Machine
                 Learning database. We also make comparisons with other
                 reported evolutionary computation techniques. On almost
                 all the benchmark datasets, the MOGP approaches give
                 better or identical performance to the best of the
                 conventional methods. Of our proposed MOGP-based
                 algorithms, we conclude that hierarchical feature
                 extraction performs best on multi-classification
  affiliation =  "Laboratory for Image and Vision Engineering,
                 Department of Electronic and Electrical Engineering,
                 University of Sheffield, Mappin Street, Sheffield, S1
                 3JD UK",

Genetic Programming entries for Yang Zhang Peter I Rockett