Learning acyclic rules based on Chaining Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong",
  title =        "Learning acyclic rules based on Chaining Genetic
  booktitle =    "The 4th ACS/IEEE International Conference on Computer
                 Systems and Applications (AICCSA-06)",
  year =         "2006",
  editor =       "Michael A. Langston and Mohsen Guizani",
  pages =        "960--967",
  address =      "Dubai",
  month =        mar # " 8-11",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-4244-0211-5",
  URL =          "http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumber=33913&arnumber=1618469&count=182&index=144",
  DOI =          "doi:10.1109/AICCSA.2006.205204",
  size =         "8 pages",
  abstract =     "Multi-class problem is the class of problems having
                 more than one classes in the data set. Bayesian Network
                 (BN) is a well-known algorithm handling the multi-class
                 problem and is applied to different areas. But BN
                 cannot handle continuous values. In contrast, Genetic
                 Programming (GP) can handle continuous values and
                 produces classification rules. However, GP is possible
                 to produce cyclic rules representing tautologic, in
                 which are useless for inference and expert systems.
                 Co-evolutionary Rule-chaining Genetic Programming
                 (CRGP) is the first variant of GP handling the
                 multi-class problem and produces acyclic classification
                 rules [16]. It employs backward chaining inference to
                 carry out classification based on the acquired acyclic
                 rule set. It can handle multi-classes; it can avoid
                 cyclic rules; it can handle input attributes with
                 continuous values; and it can learn complex
                 relationships among the attributes. In this paper, we
                 propose a novel algorithm, the Chaining Genetic
                 Programming (CGP) learning a set of acyclic rules and
                 to produce better results than the CRGP's. The
                 experimental results demonstrate that the proposed
                 algorithm has the shorter learning process and can
                 produce more accurate acyclic classification rules.",
  notes =        "http://www.cs.utk.edu/aiccsa06/",
  bibdate =      "2009-06-09",
  bibsource =    "DBLP,

Genetic Programming entries for Wing-Ho Shum Kwong-Sak Leung Man Leung Wong