Inductive data mining based on genetic programming: Automatic generation of decision trees from data for process historical data analysis

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@Article{Ma20091602,
  author =       "Chao Y. Ma and Xue Z. Wang",
  title =        "Inductive data mining based on genetic programming:
                 Automatic generation of decision trees from data for
                 process historical data analysis",
  journal =      "Computers \& Chemical Engineering",
  volume =       "33",
  number =       "10",
  pages =        "1602--1616",
  year =         "2009",
  note =         "Selected Papers from the 18th European Symposium on
                 Computer Aided Process Engineering (ESCAPE-18)",
  ISSN =         "0098-1354",
  DOI =          "doi:10.1016/j.compchemeng.2009.04.005",
  URL =          "http://www.sciencedirect.com/science/article/B6TFT-4W7420M-3/2/7984765c8dbd5fb91cfbad06b2673cd3",
  keywords =     "genetic algorithms, genetic programming, Process
                 historical data analysis, Decision trees, Decision
                 forest, Wastewater treatment plant, Inductive data
                 mining",
  abstract =     "An inductive data mining algorithm based on genetic
                 programming, GPForest, is introduced for automatic
                 construction of decision trees and applied to the
                 analysis of process historical data. GPForest not only
                 outperforms traditional decision tree generation
                 methods that are based on a greedy search strategy
                 therefore necessarily miss regions of the search space,
                 but more importantly generates multiple trees in each
                 experimental run. In addition, by varying the initial
                 values of parameters, more decision trees can be
                 generated in new experiments. From the multiple
                 decision trees generated, those with high fitness
                 values are selected to form a decision forest. For
                 predictive purpose, the decision forest instead of a
                 single tree is used and a voting strategy is employed
                 which allows the combination of the predictions of all
                 decision trees in the forest in order to generate the
                 final prediction. It was demonstrated that in
                 comparison with decision tree methods in the
                 literature, GPForest gives much improved performance.",
  notes =        "See \cite{Ma2008581}",
}

Genetic Programming entries for Cai-Yun Ma Xue Zhong Wang

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