AMPSO: A New Particle Swarm Method for Nearest Neighborhood Classification Articles uri icon

publication date

  • October 2009

start page

  • 1082

end page

  • 1091

issue

  • 5

volume

  • 39

international standard serial number (ISSN)

  • 1094-6977

electronic international standard serial number (EISSN)

  • 1558-2442

abstract

  • Nearest prototype methods can be quite successful on many pattern classification problems. In these methods, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes
    based on the nearest prototype in this collection. In this paper, we first use
    the standard particle swarm optimizer (PSO) algorithm to find those prototypes.
    Second, we present a new algorithm, called adaptive Michigan PSO (AMPSO) in
    order to reduce the dimension of the search space and provide more flexibility
    than the former in this application. AMPSO is based on a different approach to
    particle swarms as each particle in the swarm represents a single prototype in
    the solution. The swarm does not converge to a single solution; instead, each
    particle is a local classifier, and the whole swarm is taken as the solution to
    the problem. It uses modified PSO equations with both particle competition and
    cooperation and a dynamic neighborhood. As an additional feature, in AMPSO, the
    number of prototypes represented in the swarm is able to adapt to the problem,
    increasing as needed the number of prototypes and classes of the prototypes that
    make the solution to the problem. We compared the results of the standard PSO
    and AMPSO in several benchmark problems from the University of California,
    Irvine, data sets and find that AMPSO always found a better solution than the
    standard PSO. We also found that it was able to improve the results of the
    Nearest Neighbor classifiers, and it is also competitive with some of the
    algorithms most commonly used for classification.