Learning effective classifiers with Z-value measure based on genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Been-Chian Chien and Jung-Yi Lin and Wei-Pang Yang",
  title =        "Learning effective classifiers with Z-value measure
                 based on genetic programming",
  journal =      "Pattern Recognition",
  year =         "2004",
  volume =       "37",
  pages =        "1957--1972",
  number =       "10",
  abstract =     "This paper presents a learning scheme for data
                 classification based on genetic programming. The
                 proposed learning approach consists of an adaptive
                 incremental learning strategy and distance-based
                 fitness functions for generating the discriminant
                 functions using genetic programming. To classify data
                 using the discriminant functions effectively, the
                 mechanism called Z-value measure is developed. Based on
                 the Z-value measure, we give two classification
                 algorithms to resolve ambiguity among the discriminant
                 functions. The experiments show that the proposed
                 approach has less training time than previous GP
                 learning methods. The learned classifiers also have
                 high accuracy of classification in comparison with the
                 previous classifiers.",
  owner =        "wlangdon",
  URL =          "http://www.sciencedirect.com/science/article/B6V14-4CPVJFT-3/2/51f0ecbd7d198da15f4ae094e378c5d0",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1016/j.patcog.2004.03.016",

Genetic Programming entries for Been-Chian Chien Mick Jung-Yi Lin Wei-Pang Yang