# X-TREPAN: a multi class regression and adapted extraction of comprehensible decision tree in artificial neural networks

arXiv: https://arxiv.org/abs/1508.07551 · HTML: https://mlsanity.com/paper/1508.07551/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Aug 2015.

## Built on by (most cited first, 2 of 2)

- [Rule Extraction in Machine Learning: Chat Incremental Pattern Constructor](https://mlsanity.com/paper/2208.00335/) (2022)
- [Extracting PAC Decision Trees from Black Box Binary Classifiers: The Gender Bias Case Study on BERT-based Language Models](https://mlsanity.com/paper/2412.10513/) (2024)

## Abstract (arXiv)

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.