NDLI: Extracting decision trees from trained neural networks

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Extracting decision trees from trained neural networks

Content Provider	ACM Digital Library
Author	Boz, Olcay
Abstract	Neural Networks are successful in acquiring hidden knowledge in datasets. Their biggest weakness is that the knowledge they acquire is represented in a form not understandable to humans. Researchers tried to address this problem by extracting rules from trained Neural Networks. Most of the proposed rule extraction methods required specialized type of Neural Networks; some required binary inputs and some were computationally expensive. Craven proposed extracting MofN type Decision Trees from Neural Networks. We believe MofN type Decision Trees are only good for MofN type problems and trees created for regular high dimensional real world problems may be very complex. In this paper, we introduced a new method for extracting regular C4.5 like Decision Trees from trained Neural Networks. We showed that the new method (DecText) is effective in extracting high fidelity trees from trained networks. We also introduced a new discretization technique to make DecText be able to handle continuous features and a new pruning technique for finding simplest tree with the highest fidelity.
Starting Page	456
Ending Page	461
Page Count	6
File Format	PDF
ISBN	158113567X
DOI	10.1145/775047.775113
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2002-07-23
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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