GP-based secondary classifiers

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

  author =       "Ankur Teredesai and Venu Govindaraju",
  title =        "GP-based secondary classifiers",
  journal =      "Pattern Recognition",
  year =         "2005",
  volume =       "38",
  number =       "4",
  pages =        "505--512",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Handwritten
                 digit recognition, Feature selection, Classification,
                 Secondary classifiers",
  DOI =          "doi:10.1016/j.patcog.2004.06.010",
  abstract =     "Genetic programmingnext term (GP) is used to evolve
                 secondary classifiers for disambiguating between pairs
                 of handwritten digit images. The inherent property of
                 feature selection accorded by GP is exploited to make
                 sharper decision between conflicting classes.
                 Classification can be done in several steps with an
                 available feature set and a mixture of strategies. A
                 two-step classification strategy is presented in this
                 paper. After the first step of the classification using
                 the full feature set, the high confidence recognition
                 result will lead to an end of the recognition process.
                 Otherwise a secondary classifier designed using a
                 sub-set of the original feature set and the information
                 available from the earlier classification step will
                 help classify the input further. The feature selection
                 mechanism employed by GP selects important features
                 that provide maximum separability between classes under
                 consideration. In this way, a sharper decision on fewer
                 classes is obtained at the secondary classification
                 stage. The full feature set is still available in both
                 stages of classification to retain complete
                 information. An intuitive motivation and detailed
                 analysis using confusion matrices between digit classes
                 is presented to describe how this strategy leads to
                 improved recognition performance. In comparison with
                 the existing methods, our method is aimed for
                 increasing recognition accuracy and reliability.
                 Results are reported for the BHA test-set and the NIST
                 test-set of handwritten digits.",

Genetic Programming entries for Ankur M Teredesai Venugopal Govindaraju