Complexity-driven Evolution of Decision Graphs for Classification of Medical Data

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  author =       "Vili Podgorelec",
  title =        "Complexity-driven Evolution of Decision Graphs for
                 Classification of Medical Data",
  journal =      "Informatica",
  year =         "2005",
  volume =       "29",
  number =       "1",
  pages =        "41--51",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 classification, meta agents, automatic programming,
                 complexity, medical data",
  ISSN =         "0350-5596",
  URL =          "",
  size =         "11 pages",
  abstract =     "In the paper we study the possibility of constructing
                 decision graphs with the help of several meta agents.
                 Decision graphs are an extension of the well known
                 decision trees and introduce the possibility of program
                 nodes and cycles in a classification model. A
                 two-levelled evolutionary algorithm for the induction
                 of decision graphs is presented and the principle of
                 classification based on the decision graphs is
                 described. Several agents are used to construct the
                 decision graphs; they are constructed and evolved with
                 the help of automatic programming and evaluated with a
                 universal complexity measure. The developed model is
                 applied to a medical dataset for the classification of
                 patients with mitral valve prolapse syndrome.",
  notes =        "Networks

                 Institute of Informatics, University of Maribor, FERI,
                 Smetanova 17, SI-2000 Maribor, Slovenia",

Genetic Programming entries for Vili Podgorelec