Combination of support vector machines using genetic programming

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

  author =       "Abdul Majid and Asifullah Khan and Anwar M. Mirza",
  title =        "Combination of support vector machines using genetic
  journal =      "International Journal of Hybrid Intelligent Systems",
  year =         "2006",
  volume =       "3",
  number =       "2",
  pages =        "109--125",
  month =        jun,
  keywords =     "genetic algorithms, genetic programming, Support
                 vector machines, optimal composite classifiers,
                 receiver operating characteristics curves, Area Under
                 the Convex Hull (AUCH), AUROC",
  ISSN =         "1448-5869",
  URL =          "",
  DOI =          "doi:10.3233/HIS-2006-3204",
  size =         "17 pages",
  abstract =     "the combination of support vector machine (SVM)
                 classifiers using Genetic Programming (GP) for gender
                 classification problem. In our scheme, individual SVM
                 classifiers are constructed through the learning of
                 different SVM kernel functions. The predictions of SVM
                 classifiers are then combined using GP to develop
                 Optimal Composite Classifier (OCC). In this way, the
                 combined decision space is more informative and
                 discriminant. OCC has shown improved performance than
                 that of optimised individual SVM classifiers using grid
                 search. Another advantage of our GP combination scheme
                 is that it automatically incorporates the issues of
                 optimal kernel function and model selection to achieve
                 high performance classification model. The
                 classification performance is reported by using
                 Receiver Operating Characteristics (ROC) Curve.
                 Experiments are conducted under various feature sets to
                 show that OCC is more informative and robust as
                 compared to their individual SVM classifiers.
                 Specifically, it attains high margin of improvement for
                 small feature sets.",

Genetic Programming entries for Abdul Majid Asifullah Khan Anwar M Mirza