Inferencing Bayesian Networks from Time Series Data Using Natural Selection

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Andrew J. Novobilski and Farhad A. Kamangar",
  title =        "Inferencing Bayesian Networks from Time Series Data
                 Using Natural Selection",
  booktitle =    "Proceedings of the Thirteenth International Florida
                 Artificial Intelligence Research Society Conference",
  year =         "2000",
  editor =       "James N. Etheredge and Bill Z. Manaris",
  pages =        "298--302",
  address =      "Orlando, Florida, USA",
  month =        may # " 22-24",
  publisher =    "AAAI Press",
  bibsource =    "DBLP,",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 Networks, datamining",
  ISBN =         "1-57735-113-4",
  URL =          "",
  size =         "5 pages",
  abstract =     "This paper describes a new framework for using natural
                 selection to evolve Bayesian Networks for use in
                 forecasting time series data. It extends current
                 research by introducing a tree based representation of
                 a candidate Bayesian Network that addresses the problem
                 of model identification and training through the use of
                 natural selection. The framework constructs a modified
                 Naive Bayesian classifier by searching for
                 relationships within the data that will produce a model
                 for the underlying process generating the time series
                 data. Experimental results are presented that compare
                 forecasts in the presence of multiple sources of
                 information made using the naturally selected belief
                 network versus a random walk.",
  notes =        "FLAIRS 2000 Conference

Genetic Programming entries for Andrew J Novobilski Farhad A Kamangar