Genetic programming based pattern classification with feature space partitioning

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Kishore:2001:ISJ,
  author =       "J. K. Kishore and L. M. Patnaik and V. Mani and 
                 V. K. Agrawal",
  title =        "Genetic programming based pattern classification with
                 feature space partitioning",
  journal =      "Information Sciences",
  year =         "2001",
  volume =       "131",
  number =       "1-4",
  pages =        "65--86",
  month =        jan,
  email =        "lalit@micro.iisc.ernet.in",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-42MFDYH-4/2/de6209d53f2e0ec0addf0e2cfa62fd91",
  DOI =          "doi:10.1016/S0020-0255(00)00081-5",
  size =         "22 pages",
  abstract =     "Genetic programming (GP) is an evolutionary technique
                 and is gaining attention for its ability to learn the
                 underlying data relationships and express them in a
                 mathematical manner. Although GP uses the same
                 principles as genetic algorithms, it is a symbolic
                 approach to program induction; i.e., it involves the
                 discovery of a highly fit computer program from the
                 space of computer programs that produces a desired
                 output when presented with a particular input. We have
                 successfully applied the GP paradigm for the
                 n-category pattern classification problem. The
                 ability of the GP classifier to learn the data
                 distributions depends upon the number of classes and
                 the spatial spread of data. As the number of classes
                 increases, it increases the difficulty for the GP
                 classifier to resolve between classes. So, there is a
                 need to partition the feature space and identify
                 sub-spaces with reduced number of classes. The basic
                 objective is to divide the feature space into
                 sub-spaces and hence the data set that contains
                 representative samples of n classes into sub-data sets
                 corresponding to the sub-spaces of the feature space,
                 so that some of the sub-data sets/spaces can have data
                 belonging to only p-classes (pInformation Sciences
                 http://www.elsevier.com/inca/publications/store/5/0/5/7/3/0/505730.pub.htt
                 GPQUICK",
}

Genetic Programming entries for J K Kishore L M Patnaik V Mani V K Agrawal

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