A Generic Multi-dimensional Feature Extraction Method Using Multiobjective Genetic Programming

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@Article{Zhang:2009:EC,
  author =       "Yang Zhang and Peter I. Rockett",
  title =        "A Generic Multi-dimensional Feature Extraction Method
                 Using Multiobjective Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2009",
  volume =       "17",
  number =       "1",
  pages =        "89--115",
  month =        "Spring",
  keywords =     "genetic algorithms, genetic programming, MOGP, PCA",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/evco.2009.17.1.89",
  abstract =     "In this paper, we present a generic feature extraction
                 method for pattern classification using multi-objective
                 genetic programming. This not only evolves the
                 (near-)optimal set of mappings from a pattern space to
                 a multi-dimensional decision space, but also
                 simultaneously optimizes the dimensionality of that
                 decision space. The presented framework evolves
                 vector-to-vector feature extractors that maximize class
                 separability. We demonstrate the efficacy of our
                 approach by making statistically-founded comparisons
                 with a wide variety of established classifier paradigms
                 over a range of datasets and find that for most of the
                 pairwise comparisons, our evolutionary method delivers
                 statistically smaller misclassification errors. At very
                 worst, our method displays no statistical difference in
                 a few pairwise comparisons with established
                 classifier/dataset combinations; crucially, none of the
                 misclassification results produced by our method is
                 worse than any comparator classifier. Although
                 principally focused on feature extraction, feature
                 selection is also performed as an implicit side effect;
                 we show that both feature extraction and selection are
                 important to the success of our technique. The
                 presented method has the practical consequence of
                 obviating the need to exhaustively evaluate a large
                 family of conventional classifiers when faced with a
                 new pattern recognition problem in order to attain a
                 good classification accuracy.",
  notes =        "WEKA, UCI. See also \cite{10.1.1.99.3617}",
}

Genetic Programming entries for Yang Zhang Peter I Rockett

Citations