An investigation of TREPAN utilising a continuous oracle model

Authors:
William A. Young II;Gary R. Weckman;Maimuna H. Rangwala;Harry S. Whiting II;Helmut W. Paschold;Andrew H. Snow;Chad L. Mourning
Affiliations:
Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA.;Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA.;Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA.;Department of Industrial and Systems Engineering, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA.;Department of Social and Public Health, College of Health Sciences and Professions, Ohio University, Athens, OH 45701, USA.;J. Warren McClure School of Information and Telecommunication Systems, College of Communication, Ohio University, Athens, OH 45701, USA.;School of Electrical Engineering and Computer Science, Russ College of Engineering and Technology, Ohio University, Athens, OH 45701, USA
Venue:
International Journal of Data Analysis Techniques and Strategies
Year:
2011

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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.