Transforming backpropagation neural networks to decision trees using NN-DT cascade method

Authors:
Milan Zorman;Peter Kokol;Bruno Stiglic
Affiliations:
Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia;Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia;Laboratory for System Design, Faculty of Electrical Engineering and Computer Science, University of Maribor, SI-2000 Maribor, Slovenia
Venue:
Second international workshop on Intelligent systems design and application
Year:
2002

Citing 1
Cited 1

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Abstract

Transforming knowledge between connectionist and symbolic machine learning approaches is not a uniform task and there is no general recipe for performing it. Irrespective of the way the transformation of knowledge, the developed methods usually fall into two classes: methods with high rate of transformed knowledge is done, but with a lot of restrictions concerning the type of data in the training sets, training methods, and the size of data sets; and methods with moderate rate of transformed knowledge, but with less restrictions. Our main interest was to find or develop a technique that would possess the knowledge acquisition power of neural networks and explanation power of decision trees. That is why we developed a NN-DT Cascade method, which is capable of transforming a part of knowledge from the neural network into a decision tree.