Designing efficient discriminant functions for multi-category classification using evolutionary methods

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

@Article{Soltani:2016:Neurocomputing,
  author =       "Abolfazl Soltani and Seyed Mohammad Ahadi and 
                 Neda Faraji and Saeed Sharifian",
  title =        "Designing efficient discriminant functions for
                 multi-category classification using evolutionary
                 methods",
  journal =      "Neurocomputing",
  volume =       "173, Part 3",
  pages =        "1885--1897",
  year =         "2016",
  ISSN =         "0925-2312",
  DOI =          "doi:10.1016/j.neucom.2015.08.093",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0925231215013946",
  abstract =     "In this paper, we propose two approaches to obtain
                 accurate classifiers for dealing with multi-category
                 classification problem. Our work is based on one-vs-all
                 strategy where we try to decrease conflicting
                 situations. In the first phase of both approaches we
                 employ Genetic Programming to find populations of the
                 best discriminant functions (one population for each
                 class). In addition to traditional function set, like {
                 + , - , a ,/ } , we use other special functions in our
                 binary trees. We also use both negative and positive
                 constants in the terminal nodes of the trees. In the
                 second phase, we employ Ant Colony in our first
                 approach, called GP-Ant, and Genetic Algorithm in the
                 second one, called GP-GA, to find the best combination
                 of discriminant functions found in the previous phase.
                 We also provide a special modification box to modify
                 the decision of our final integrated classifiers, when
                 conflicting situations happen. To cope with conflicting
                 situations, we also use an appropriate fitness function
                 in the second phase. We compare our works with both
                 state of the art and basic multi-category
                 classification methods on eight well-known publicly
                 available data sets. Our experimental results show that
                 our methods are statistically significantly better than
                 all the other classification methods used.",
  keywords =     "genetic algorithms, genetic programming,
                 Multi-category classification, Ant colony system,
                 Discriminant functions, One-vs-all strategy",
}

Genetic Programming entries for Abolfazl Soltani Seyed Mohammad Ahadi Neda Faraji Saeed Sharifian

Citations