X-TREPAN : A Multi Class Regression and Adapted Extraction of Comprehensible Decision Tree in Artificial Neural Network

摘要

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