Multi-objective approach based on grammar-guided genetic programming for solving multiple instance problems

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Amelia Zafra and Sebastian Ventura",
  title =        "Multi-objective approach based on grammar-guided
                 genetic programming for solving multiple instance
  journal =      "Soft Computing - A Fusion of Foundations,
                 Methodologies and Applications",
  year =         "2012",
  number =       "6",
  volume =       "16",
  pages =        "955--977",
  keywords =     "genetic algorithms, genetic programming, multiple
                 instance learning, multiple objective learning, grammar
                 guided genetic programming, evolutionary rule
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-011-0794-0",
  size =         "23 pages",
  abstract =     "Multiple instance learning (MIL) is considered a
                 generalisation of traditional supervised learning which
                 deals with uncertainty in the information. Together
                 with the fact that, as in any other learning framework,
                 the classifier performance evaluation maintains a
                 trade-off relationship between different conflicting
                 objectives, this makes the classification task less
                 straightforward. This paper introduces a
                 multi-objective proposal that works in a MIL scenario
                 to obtain well-distributed Pareto solutions to
                 multi-instance problems. The algorithm developed,
                 Multi-Objective Grammar Guided Genetic Programming for
                 Multiple Instances (MOG3P-MI), is based on
                 grammar-guided genetic programming, which is a robust
                 tool for classification. Thus, this proposal combines
                 the advantages of the grammar-guided genetic
                 programming with benefits provided by multi-objective
                 approaches. First, a study of multi-objective
                 optimisation for MIL is carried out. To do this, three
                 different extensions of MOG3P-MI are designed and
                 implemented and their performance is compared. This
                 study allows us on the one hand, to check the
                 performance of multi-objective techniques in this
                 learning paradigm and on the other hand, to determine
                 the most appropriate evolutionary process for MOG3P-MI.
                 Then, MOG3P-MI is compared with some of the most
                 significant proposals developed throughout the years in
                 MIL. Computational experiments show that MOG3P-MI often
                 obtains consistently better results than the other
                 algorithms, achieving the most accurate models.
                 Moreover, the classifiers obtained are very
  affiliation =  "Department of Computer Science and Numerical Analysis,
                 University of Cordoba, Cordoba, Spain",
  bibdate =      "2012-05-14",
  bibsource =    "DBLP,

Genetic Programming entries for Amelia Zafra Gomez Sebastian Ventura