Learning SVM with Complex Multiple Kernels Evolved by Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Diosan:2010:JAIT,
  author =       "Laura Diosan and Alexandrina Rogozan and 
                 Jean Pierre Pecuchet",
  title =        "Learning SVM with Complex Multiple Kernels Evolved by
                 Genetic Programming",
  journal =      "International Journal on Artificial Intelligence
                 Tools",
  year =         "2010",
  volume =       "19",
  number =       "5",
  pages =        "647--677",
  keywords =     "genetic algorithms, genetic programming, Multiple
                 kernel learning, hybrid model, SVM",
  DOI =          "doi:10.1142/S0218213010000352",
  abstract =     "Classic kernel-based classifiers use only a single
                 kernel, but the real-world applications have emphasised
                 the need to consider a combination of kernels, also
                 known as a multiple kernel (MK), in order to boost the
                 classification accuracy by adapting better to the
                 characteristics of the data. Our purpose is to
                 automatically design a complex multiple kernel by
                 evolutionary means. In order to achieve this purpose we
                 propose a hybrid model that combines a Genetic
                 Programming (GP) algorithm and a kernel-based Support
                 Vector Machine (SVM) classifier. In our model, each GP
                 chromosome is a tree that encodes the mathematical
                 expression of a multiple kernel. The evolutionary
                 search process of the optimal MK is guided by the
                 fitness function (or efficiency) of each possible MK.
                 The complex multiple kernels which are evolved in this
                 manner (eCMKs) are compared to several classic simple
                 kernels (SKs), to a convex linear multiple kernel
                 (cLMK) and to an evolutionary linear multiple kernel
                 (eLMK) on several real-world data sets from UCI
                 repository. The numerical experiments show that the SVM
                 involving the evolutionary complex multiple kernels
                 perform better than the classic simple kernels.
                 Moreover, on the considered data sets, the new multiple
                 kernels outperform both the cLMK and eLMK linear
                 multiple kernels. These results emphasise the fact that
                 the SVM algorithm requires a combination of kernels
                 more complex than a linear one in order to boost its
                 performance.",
  notes =        "IJAIT Laboratoire d'Informatique, de Traitement de
                 l'Information et des Systemes, EA 4108, Institut
                 National des Sciences Appliquees, Rouen, France",
}

Genetic Programming entries for Laura Diosan Alexandrina Rogozan Jean Pierre Pecuchet

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