A multi-objective genetic programming approach to developing Pareto optimal decision trees

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@Article{Zhao:2007:DSS,
  author =       "Huimin Zhao",
  title =        "A multi-objective genetic programming approach to
                 developing Pareto optimal decision trees",
  journal =      "Decision Support Systems",
  year =         "2007",
  volume =       "43",
  number =       "3",
  pages =        "809--826",
  month =        apr,
  keywords =     "genetic algorithms, genetic programming, Data mining,
                 Binary classification, Decision tree, Cost-sensitive
                 classification, Multi-objective optimisation, Pareto
                 optimality",
  DOI =          "doi:10.1016/j.dss.2006.12.011",
  abstract =     "Classification is a frequently encountered data mining
                 problem. Decision tree techniques have been widely used
                 to build classification models as such models closely
                 resemble human reasoning and are easy to understand.
                 Many real-world classification problems are
                 cost-sensitive, meaning that different types of
                 misclassification errors are not equally costly. Since
                 different decision trees may excel under different cost
                 settings, a set of non-dominated decision trees should
                 be developed and presented to the decision maker for
                 consideration, if the costs of different types of
                 misclassification errors are not precisely determined.
                 This paper proposes a multi-objective genetic
                 programming approach to developing such alternative
                 Pareto optimal decision trees. It also allows the
                 decision maker to specify partial preferences on the
                 conflicting objectives, such as false negative vs.
                 false positive, sensitivity vs. specificity, and recall
                 vs. precision, to further reduce the number of
                 alternative solutions. A diabetes prediction problem
                 and a credit card application approval problem are used
                 to illustrate the application of the proposed
                 approach.",
}

Genetic Programming entries for Huimin Zhao

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