A Generic Optimal Feature Extraction Method using Multiobjective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Yang Zhang and Peter I. Rockett",
  title =        "A Generic Optimal Feature Extraction Method using
                 Multiobjective Genetic Programming",
  institution =  "Department of Electronic and Electrical Engineering,
                 University of Sheffield",
  year =         "2006",
  number =       "VIE 2006/001",
  address =      "UK",
  keywords =     "genetic algorithms, genetic programming, Feature
                 Extraction, Multiobjective Optimisation, MOGP, Pattern
  URL =          "http://www.shef.ac.uk/eee/vie/tech/VIE2006-002.pdf",
  abstract =     "In this paper, we present a generic, optimal feature
                 extraction method using multiobjective genetic
                 programming. We reexamine the feature extraction
                 problem and argue that effective feature extraction can
                 significantly enhance the performance of pattern
                 recognition systems with simple classifiers. A
                 framework is presented to evolve optimised feature
                 extractors that transform an input pattern space into a
                 decision space in which maximal class separability is
                 obtained. We have applied this method to real world
                 datasets from the UCI Machine Learning and StatLog
                 databases to verify our approach and compare our
                 proposed method with other reported results. We
                 conclude that our algorithm is able to produce
                 classifiers of superior (or equivalent) performance to
                 the conventional classifiers examined, suggesting
                 removal of the need to exhaustively evaluate a large
                 family of conventional classifiers on any new
  size =         "29 pages",

Genetic Programming entries for Yang Zhang Peter I Rockett