Two-stage learning for multi-class classification using genetic programming

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

@Article{Jabeen:2013:Neurocomputing,
  author =       "Hajira Jabeen and Abdul Rauf Baig",
  title =        "Two-stage learning for multi-class classification
                 using genetic programming",
  journal =      "Neurocomputing",
  volume =       "116",
  month =        "20 " # sep,
  pages =        "311--316",
  year =         "2013",
  note =         "Advanced Theory and Methodology in Intelligent
                 Computing Selected Papers from the Seventh
                 International Conference on Intelligent Computing (ICIC
                 2011).",
  keywords =     "genetic algorithms, genetic programming,
                 Classification, Classifier, Expression, Rule,
                 Algorithm",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2012.01.048",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231212007308",
  abstract =     "This paper introduces a two-stage strategy for
                 multi-class classification problems. The proposed
                 technique is an advancement of tradition binary
                 decomposition method. In the first stage, the
                 classifiers are trained for each class versus the
                 remaining classes. A modified fitness value is used to
                 select good discriminators for the imbalanced data. In
                 the second stage, the classifiers are integrated and
                 treated as a single chromosome that can classify any of
                 the classes from the dataset. A population of such
                 classifier-chromosomes is created from good classifiers
                 (for individual classes) of the first phase. This
                 population is evolved further, with a fitness that
                 combines accuracy and conflicts. The proposed method
                 encourages the classifier combination with good
                 discrimination among all classes and less conflicts.
                 The two-stage learning has been tested on several
                 benchmark datasets and results are found encouraging.",
}

Genetic Programming entries for Hajira Jabeen Abdul Rauf Baig

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