A Novel Approach to Design Classifier Using Genetic Programming

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

@Article{muni:2004:TEC,
  author =       "Durga Prasad Muni and Nikhil R Pal and Jyotirmay Das",
  title =        "A Novel Approach to Design Classifier Using Genetic
                 Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2004",
  volume =       "8",
  number =       "2",
  pages =        "183--196",
  month =        apr,
  email =        "muni_r@isical.ac.in, nikhi@isical.ac.in,
                 jdas@isical.ac.in",
  keywords =     "genetic algorithms, genetic programming,
                 classification, multi-tree concept",
  URL =          "http://www.isical.ac.in/~muni_r",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.205.7129",
  DOI =          "doi:10.1109/TEVC.2004.825567",
  size =         "14 pages",
  abstract =     "We propose a new approach for designing classifiers
                 for a c-class c>=2 problem using Genetic Programming
                 (GP). The proposed approach takes an integrated view of
                 all classes when the GP evolves. A multi-tree
                 representation of chromosomes is used. In this context,
                 we propose a modified crossover operation and a new
                 mutation operation that reduces the destructive nature
                 of conventional genetic operations. A new concept of
                 unfitness of a tree is used to select trees for genetic
                 operations. This gives more opportunity for unfit trees
                 to become fit. A new concept of OR-ing chromosomes in
                 the terminal population is introduced, which enables us
                 to get a classifier with better performance. Finally, a
                 weight based scheme and a heuristic rule based scheme
                 characterising typical mistakes are used for conflict
                 resolution. The classifier is capable of saying ``don't
                 know'' when faced with unfamiliar examples. The
                 effectiveness of our scheme is demonstrated on several
                 real data sets.",
  notes =        "UCI machine learning benchmarks",
}

Genetic Programming entries for Durga Prasad Muni Nikhil Ranjan Pal Jyotirmay Das

Citations