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Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks

Diederich, Joachim, Tickle, Alan B., & Geva, Shlomo (2010) Quo Vadis? Reliable and Practical Rule Extraction from Neural Networks. In Advances in Machine Learning I. Springer, Berlin ; Heidelberg, pp. 479-490.

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Abstract

Rule extraction from neural network algorithms have been investigated for two decades and there have been significant applications. Despite this level of success, rule extraction from neural network methods are generally not part of data mining tools, and a significant commercial breakthrough may still be some time away. This paper briefly reviews the state-of-the-art and points to some of the obstacles, namely a lack of evaluation techniques in experiments and larger benchmark data sets. A significant new development is the view that rule extraction from neural networks is an interactive process which actively involves the user. This leads to the application of assessment and evaluation techniques from information retrieval which may lead to a range of new methods.

Impact and interest:

2 citations in Scopus
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ID Code: 48154
Item Type: Book Chapter
DOI: 10.1007/978-3-642-05177-7
ISBN: 978-3-642-05176-0
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Current > Institutes > Institute for Future Environments
Deposited On: 19 Jan 2012 12:33
Last Modified: 01 Mar 2012 00:30

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