Dynamic Split-Point Selection Method for Decision Tree Evolved by Gene Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Qu Li and Min Yao and Weihong Wang and 
                 Xiaohong Cheng",
  title =        "Dynamic Split-Point Selection Method for Decision Tree
                 Evolved by Gene Expression Programming",
  booktitle =    "2009 IEEE Congress on Evolutionary Computation",
  year =         "2009",
  editor =       "Andy Tyrrell",
  pages =        "736--740",
  address =      "Trondheim, Norway",
  month =        "18-21 " # may,
  organization = "IEEE Computational Intelligence Society",
  publisher =    "IEEE Press",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, C4.5, classification accuracy,
                 decision tree, dynamic split-point selection method,
                 evolutionary computation theory, heuristic method,
                 optimal split points, tree splitting, data handling,
                 decision trees",
  isbn13 =       "978-1-4244-2959-2",
  file =         "P196.pdf",
  DOI =          "doi:10.1109/CEC.2009.4983018",
  abstract =     "Gene Expression Programming(GEP) is a kind of
                 heuristic method based on evolutionary computation
                 theory. GEP has been used to evolve parsimonious
                 decision tree with high accuracy comparable to C4.5.
                 However, the basic GEPDT do not distinguish different
                 attributes, whose boundaries are usually quite
                 different. The basic GEPDT often fails to find optimal
                 split points for some branches and thus handicapped the
                 learning tasks. In this paper, we proposed a simple but
                 effective Split-point Selection Method for GEP evolved
                 decision tree to improve the performance of tree
                 splitting and classification accuracy. Results show
                 that our method can find better generalized ability
                 rules and it is especially suitable for difficult
                 problems with many attributes in different
  notes =        "CEC 2009 - A joint meeting of the IEEE, the EPS and
                 the IET. IEEE Catalog Number: CFP09ICE-CDR. Also known
                 as \cite{4983018}",

Genetic Programming entries for Qu Li Min Yao Weihong Wang Xiaohong Cheng