  In this work, the TREPAN algorithm is enhanced and extended for extracting
decision trees from neural networks. We empirically evaluated the performance
of the algorithm on a set of databases from real world events. This benchmark
enhancement was achieved by adapting Single-test TREPAN and C4.5 decision tree
induction algorithms to analyze the datasets. The models are then compared with
X-TREPAN for comprehensibility and classification accuracy. Furthermore, we
validate the experimentations by applying statistical methods. Finally, the
modified algorithm is extended to work with multi-class regression problems and
the ability to comprehend generalized feed forward networks is achieved.
