Generate classifier for Genetic Programming of Multicategory Pattern Classification Using Multiclass Microarray Datasets

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

@Article{Mansuri:2013:ijarcs,
  author =       "Anwar Mohd Mansuri and Deepali Kelkar",
  title =        "Generate classifier for Genetic Programming of
                 Multicategory Pattern Classification Using Multiclass
                 Microarray Datasets",
  journal =      "International Journal of Advanced Research in Computer
                 Science",
  year =         "2013",
  keywords =     "genetic algorithms, genetic programming, microarray,
                 classifier, mutation, crossover",
  ISSN =         "0976-5697",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:8fe02df864e6a4d6368faf194ea13abd",
  URL =          "http://www.ijarcs.info/Mansuri:2013:ijarcs.pdf",
  size =         "4 pages",
  abstract =     "In this paper a multiclass classification problem
                 solving technique based on genetic programming is
                 presented. This paper explores the feasibility of
                 applying genetic programming (GP) to multicategory
                 pattern classification. GP can discover relationships
                 among observed data and express them mathematically
                 Feature selection approaches have been widely applied
                 to deal with the small sample size problem in the
                 analysis of microarray datasets. Multiclass problem,
                 the proposed methods are based on the idea of selecting
                 a gene subset to distinguish all classes. However, it
                 will be more effective to solve a multiclass problem by
                 splitting it into a set of two- class problems and
                 solving each problem with a respective classification
                 system, Data mining deals with the problem of
                 discovering novel and interesting knowledge from large
                 amount of data. The results obtained show that by
                 applying Modified crossover together with Point
                 Mutation improves the performance of the classifier. A
                 comparison with the results achieved by other
                 techniques on a classical benchmark set is carried
                 out.",
}

Genetic Programming entries for Anwar Mohd Mansuri Deepali Kelkar

Citations