X-TREPAN: a multi class regression and adapted extraction of comprehensible decision tree in artificial neural networks
This work addresses the need for interpretable models in machine learning by extending an existing method to handle multi-class regression and generalized feed-forward networks, though it is incremental in nature.
The authors enhanced the TREPAN algorithm to extract decision trees from neural networks, achieving improved comprehensibility and classification accuracy on real-world datasets, with validation through statistical methods.
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.