Dimension Reduction Using Evolutionary Support Vector Machines

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "J. H. Ang and E. J. Teoh and C. H. Tan and 
                 K. C. Goh and K. C. Tan",
  title =        "Dimension Reduction Using Evolutionary Support Vector
  booktitle =    "2008 IEEE World Congress on Computational
  year =         "2008",
  editor =       "Jun Wang",
  pages =        "3634--3641",
  address =      "Hong Kong",
  month =        "1-6 " # jun,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  isbn13 =       "978-1-4244-1823-7",
  file =         "EC0777.pdf",
  DOI =          "doi:10.1109/CEC.2008.4631290",
  abstract =     "This paper presents a novel approach of hybridising
                 two conventional machine learning algorithms for
                 dimension reduction. Genetic Algorithm (GA) and Support
                 Vector Machines (SVMs) are integrated effectively based
                 on a wrapper approach. Specifically, the GA component
                 searches for the best attribute set using principles of
                 evolutionary process, after which the reduced dataset
                 is presented to the SVMs. Simulation results show that
                 GA-SVM hybrid is able to produce good classification
                 accuracy and a high level of consistency. In addition,
                 improvements are made to the hybrid by using a
                 correlation measure between attributes as a fitness
                 measure to replace the weaker members in the population
                 with newly formed chromosomes. This correlation measure
                 injects greater diversity and increases the overall
                 fitness of the population",
  keywords =     "genetic algorithms, genetic programming",
  notes =        "WCCI 2008 - A joint meeting of the IEEE, the INNS, the
                 EPS and the IET.",

Genetic Programming entries for J H Ang E J Teoh Chin Hiong Tan K C Goh Kay Chen Tan