X-TREPAN: An Extended Trepan for Comprehensibility and Classification Accuracy in Artificial Neural Networks
Awudu Karim, Shangbo Zhou · International Journal of Artificial Intelligence & Applications · 2015
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.