M. Craven & J. Shavlik (1999).
Rule Extraction: Where Do We Go from Here?. Department of Computer Sciences, University of Wisconsin, Machine Learning Research Group Working Paper 99-1.
This publication is available in PDF and available in postscript.
We argue that despite being an actively researched area for nearly a decade, rule-extraction technology has not made as significant of an impact as it should have. A confluence of trends, however, has made the ability to extract comprehensible descriptions from complex learned models more important now than ever. We argue that rule-extraction methods can have a significant impact in the overlapping data-mining, machine-learning and neural-network communities if research is focused on several commonly overlooked issues. We then briefly describe how we have tried to address these issues in our own work.
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