Learning Functional Dependency Networks Based on Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{conf/icdm/ShumLW05,
  title =        "Learning Functional Dependency Networks Based on
                 Genetic Programming",
  author =       "Wing-Ho Shum and Kwong-Sak Leung and Man Leung Wong",
  year =         "2005",
  pages =        "394--401",
  publisher =    "IEEE Computer Society",
  booktitle =    "Proceedings of the 5th IEEE International Conference
                 on Data Mining (ICDM 2005)",
  address =      "Houston, Texas, USA",
  month =        "27-30 " # nov,
  bibdate =      "2005-12-21",
  bibsource =    "DBLP,
                 http://dblp.uni-trier.de/db/conf/icdm/icdm2005.html#ShumLW05",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "0-7695-2278-5",
  URL =          "http://cptra.ln.edu.hk/~mlwong/conference/icdm2005.pdf",
  DOI =          "doi:10.1109/ICDM.2005.86",
  abstract =     "Bayesian Network (BN) is a powerful network model,
                 which represents a set of variables in the domain and
                 provides the probabilistic relationships among them.
                 But BN can handle discrete values only; it cannot
                 handle continuous, interval and ordinal ones, which
                 must be converted to discrete values and the order
                 information is lost. Thus, BN tends to have higher
                 network complexity and lower understandability. In this
                 paper, we present a novel dependency network which can
                 handle discrete, continuous, interval and ordinal
                 values through functions; it has lower network
                 complexity and stronger expressive power; it can
                 represent any kind of relationships; and it can
                 incorporate a-priori knowledge though user-defined
                 functions. We also propose a novel Genetic Programming
                 (GP) to learn dependency networks. The novel GP does
                 not use any knowledge-guided nor application-oriented
                 operator, thus it is robust and easy to replicate. The
                 experimental results demonstrate that the novel GP can
                 successfully discover the target novel dependency
                 networks, which have the highest accuracy and the
                 lowest network complexity.",
}

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

Citations