Kernel evolution for support vector classification

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@InProceedings{Alizadeh:2011:EAIS,
  author =       "Mehrdad Alizadeh and Mohammad Mehdi Ebadzadeh",
  title =        "Kernel evolution for support vector classification",
  booktitle =    "IEEE Workshop on Evolving and Adaptive Intelligent
                 Systems (EAIS 2011)",
  year =         "2011",
  month =        "11-15 " # apr,
  pages =        "93--99",
  address =      "Paris",
  size =         "7 pages",
  abstract =     "Support vector machines (SVMs) have been used in a
                 variety of classification tasks. SVMs undoubtedly are
                 one of the most effective classifiers in several data
                 mining applications. Determination of a kernel function
                 and related parameters has been a bottleneck for this
                 group of classifiers. In this paper a novel approach is
                 proposed to use genetic programming (GP) to design
                 domain-specific and optimal kernel functions for
                 support vector classification (SVC) which automatically
                 adjusts the parameters. Complex low dimensional mapping
                 function is evolved using GP to construct an optimal
                 linear and Gaussian kernel functions in new feature
                 space. By using the principled kernel closure
                 properties, these basic kernels are then used to evolve
                 more optimal kernels. To evaluate the proposed method,
                 benchmark datasets from UCI are applied. The result
                 indicates that for some cases the proposed methods can
                 find a more optimal solution than evolving known
                 kernels.",
  keywords =     "genetic algorithms, genetic programming, Gaussian
                 kernel functions, automatic parameter adjustment,
                 classification task, data mining application,
                 domain-specific kernel functions, feature space, kernel
                 evolution, low dimensional mapping function, optimal
                 kernel functions, optimal linear functions, principled
                 kernel closure properties, support vector
                 classification, support vector machines, Gaussian
                 processes, data mining, pattern classification, support
                 vector machines",
  DOI =          "doi:10.1109/EAIS.2011.5945924",
  notes =        "Also known as \cite{5945924}",
}

Genetic Programming entries for Mehrdad Alizadeh Mohammad Mehdi Ebadzadeh

Citations