document: Extracting decision trees from trained neural networks 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 .
doc_bio_tags: O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O