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An investigation of TREPAN utilising a continuous oracle model

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Author Info

  • William A. Young II
  • Gary R. Weckman
  • Maimuna H. Rangwala
  • Harry S. Whiting II
  • Helmut W. Paschold
  • Andrew H. Snow
  • Chad L. Mourning
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    Abstract

    TREPAN is decision tree algorithm that utilises artificial neural networks (ANNs) in order to improve partitioning conditions when sample data is sparse. When sample sizes are limited during the tree-induction process, TREPAN relies on an ANN oracle in order to create artificial sample instances. The original TREPAN implementation was limited to ANNs that were designed to be classification models. In other words, TREPAN was incapable of building decision trees from ANN models that were continuous in nature. Thus, the objective of this research was to modify the original implementation of TREPAN in order to develop and test decision trees derived from continuous-based ANN models. Though the modification were minor, they are significant because it provides researchers and practitioners an additional strategy to extract knowledge from a trained ANN regardless of its design. This research also explores how TEPAN's adjustable settings influence predictive performances based on a dataset's complexity and size.

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    Bibliographic Info

    Article provided by Inderscience Enterprises Ltd in its journal Int. J. of Data Analysis Techniques and Strategies.

    Volume (Year): 3 (2011)
    Issue (Month): 4 ()
    Pages: 325-352

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    Handle: RePEc:ids:injdan:v:3:y:2011:i:4:p:325-352

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    Web page: http://www.inderscience.com/browse/index.php?journalID=282

    Related research

    Keywords: multi-class classification; decision trees; artificial neural networks; ANNs; TREPAN; C4.5; multilayer perceptron; MLP; generalised feed-forward; GFF; modular networks; genetic algorithms; techniques; strategies; continuous oracle; data analysis.;

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