Particle swarm optimisation for object classification

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "H. Evans and Mengjie Zhang",
  title =        "Particle swarm optimisation for object
  booktitle =    "23rd International Conference Image and Vision
                 Computing New Zealand, IVCNZ 2008",
  year =         "2008",
  month =        nov,
  pages =        "1--6",
  keywords =     "genetic algorithms, genetic programming, PSO, feature
                 partitioning, noise factor, object classification,
                 optimal partition matrix, particle swarm optimisation,
                 weight matrix, feature extraction, image
                 classification, object detection, particle swarm
  DOI =          "doi:10.1109/IVCNZ.2008.4762143",
  abstract =     "This paper describes a new approach to the use of
                 particle swarm optimisation (PSO) for object
                 classification problems. Instead of using PSO to evolve
                 only a set of good parameter values for another machine
                 learning method for object classification, the new
                 approach developed in this paper can be used as a stand
                 alone method for classification. Two new methods are
                 developed in the new approach. The first new PSO method
                 treats all different features equally important and
                 finds an optimal partition matrix to separate a data
                 set into distinct class groups. The second new PSO
                 method considers the relative importance of each
                 feature with the noise factor, and evolves a weight
                 matrix to mitigate the effects of noisy partitions and
                 feature dimensions. The two methods are examined and
                 compared with a popular method using PSO combined with
                 the nearest centroid and another evolutionary computing
                 method, genetic programming, on three image data sets
                 of increasing difficulty. The results suggest that the
                 new weighted PSO method outperforms these existing
                 methods on these object classification problems.",
  notes =        "Refers to \cite{zhang:2004:eurogp} Also known as

Genetic Programming entries for Hamish Evans Mengjie Zhang