Function approximations by coupling neural networks and genetic programming trees with oblique decision trees

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@Article{YunSeogYeun:1999:AIE,
  author =       "Yun Seog Yeun and K. H. Lee and Y. S. Yang",
  title =        "Function approximations by coupling neural networks
                 and genetic programming trees with oblique decision
                 trees",
  journal =      "Artificial Intelligence in Engineering",
  year =         "1999",
  volume =       "13",
  number =       "3",
  pages =        "223--239",
  month =        jul,
  email =        "yeonyun@road.daejin.ac.kr",
  keywords =     "genetic algorithms, genetic programming, Federated
                 agents, Oblique decision tree, OC1",
  URL =          "http://members.kr.inter.net/yyshuj/paper/aie.zip",
  ISSN =         "0954-1810",
  DOI =          "doi:10.1016/S0954-1810(98)00015-6",
  URL =          "http://www.sciencedirect.com/science/article/B6V1X-3WWT8F6-3/1/2e564ef70743de81b8e3369fb01b406e",
  abstract =     "This paper is concerning the development of multiple
                 neural networks system combined with genetic
                 programming (GP) trees for problem domains where the
                 complete input space can be decomposed into several
                 different regions, and these are well represented in
                 form of oblique decision tree. The overall architecture
                 of hybrid system, called the federated agents, consists
                 of a facilitator, local agents, and boundary agents.
                 Neural networks used as local agents, each of which is
                 expert at different subregions, and GP trees serve as
                 boundary agents. A boundary agent refer to the one that
                 is specialized at only the borders of subregions where
                 discontinuities or different patterns may exist. The
                 facilitator is responsible for choosing the local agent
                 that is suitable for the given input data using
                 information obtained from oblique decision tree
                 representing a divided input space. However, there are
                 large possibility of selecting the invalid local agent
                 due to the incorrect prediction of decision tree,
                 provided that input data is close enough to the
                 boundaries of regions. Such a situation can lead
                 federated agents to produce a much higher prediction
                 error than that of a single neural network trained over
                 all input space. To deal with this, the approach taken
                 in this paper is to make the facilitator select the
                 boundary agent instead of the local agent when input
                 data is closely located to the certain border of
                 regions. In this way, even if the result of decision
                 tree may be incorrect, the results of system are less
                 affected by it. The validity of our approach is
                 examined and verified by applying the federated agents
                 to the configuration design of a midship section of
                 bulk cargo ships.",
  size =         "17 pages",
  notes =        "Linear associative memories [Kohonen,1988] set
                 numerical parameters in GP trees with overfitting
                 avoidance.

                 Training set partitioned using {"}domain knowledge or
                 clustering methods{"} p255. Separate ANN trained on
                 each subset.

                 ",
}

Genetic Programming entries for Yun Seog Yeun Kyung Ho Lee Young-Soon Yang

Citations