Combining classifiers generated by multi-gene genetic programming for protein fold recognition using genetic algorithm

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

@Article{Bardsiri:2015:IJBRA,
  title =        "Combining classifiers generated by multi-gene genetic
                 programming for protein fold recognition using genetic
                 algorithm",
  author =       "Mahshid Khatibi Bardsiri and Mahdi Eftekhari and 
                 Reza Mousavi",
  journal =      "Int. J. of Bioinformatics Research and Applications",
  publisher =    "Inderscience Publishers",
  year =         "2015",
  month =        mar # "~17",
  volume =       "11",
  number =       "2",
  pages =        "171--186",
  keywords =     "genetic algorithms, genetic programming, multi-gene
                 genetic programming, protein fold recognition,
                 bioinformatics, weighted voting, classifiers,
                 classification accuracy",
  ISSN =         "1744-5493",
  bibsource =    "OAI-PMH server at www.inderscience.com",
  URL =          "http://www.inderscience.com/link.php?id=68092",
  DOI =          "doi:10.1504/IJBRA.2015.068092",
  abstract =     "In this study the problem of protein fold recognition,
                 that is a classification task, is solved via a hybrid
                 of evolutionary algorithms namely multi-gene Genetic
                 Programming (GP) and Genetic Algorithm (GA). Our
                 proposed method consists of two main stages and is
                 performed on three datasets taken from the literature.
                 Each dataset contains different feature groups and
                 classes. In the first step, multi-gene GP is used for
                 producing binary classifiers based on various feature
                 groups for each class. Then, different classifiers
                 obtained for each class are combined via weighted
                 voting so that the weights are determined through GA.
                 At the end of the first step, there is a separate
                 binary classifier for each class. In the second stage,
                 the obtained binary classifiers are combined via GA
                 weighting in order to generate the overall classifier.
                 The final obtained classifier is superior to the
                 previous works found in the literature in terms of
                 classification accuracy.",
  notes =        "PMID: 25786796 [PubMed - in process]",
}

Genetic Programming entries for Mahshid Khatibi Bardsiri Mehdi Eftekhari Reza Mousavi

Citations