Evolutionary combination of kernels for nonlinear feature transformation

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Behzad Zamani and Ahmad Akbari and Babak Nasersharif",
  title =        "Evolutionary combination of kernels for nonlinear
                 feature transformation",
  journal =      "Information Sciences",
  year =         "2014",
  volume =       "274",
  pages =        "95--107",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, Kernel
                 principal component analysis (KPCA), Kernel linear
                 discriminant analysis (KLDA), Kernel combination",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0020025514002539",
  DOI =          "doi:10.1016/j.ins.2014.02.140",
  size =         "13 pages",
  abstract =     "The performance of kernel-based feature transformation
                 methods depends on the choice of kernel function and
                 its parameters. In addition, most of these methods do
                 not consider the classification information and error
                 for the mapping features. In this paper, we propose to
                 determine a kernel function for kernel principal
                 components analysis (KPCA) and kernel linear
                 discriminant analysis (KLDA), considering the
                 classification information. To this end, we combine the
                 conventional kernel functions using genetic algorithm
                 and genetic programming in linear and non-linear forms,
                 respectively. We use the classification error and the
                 mutual information between features and classes in the
                 kernel feature space as evolutionary fitness functions.
                 The proposed methods are evaluated on the basis of the
                 University of California Irvine (UCI) datasets and
                 Aurora2 speech database. We evaluate the methods using
                 clustering validity indices and classification
                 accuracy. The experimental results demonstrate that
                 KPCA using a nonlinear combination of kernels based on
                 genetic programming and the classification error
                 fitness function outperforms conventional KPCA using
                 Gaussian kernel and also KPCA using linear combination
                 of kernels.",

Genetic Programming entries for Behzad Zamani Ahmad Akbari Babak Nasersharif