ANN-DT: an algorithm for extraction of decision trees from artificial neural networks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explaining Explanations: An Overview of Interpretability of Machine Learning (2018, methodology): “Another decision tree method is ANN-DT [25] which uses sampling to create a decision tree: the key idea is to use sampling to expand training using a nearest neighbor method.”
Rule Extraction from Support Vector Machines: An Overview of Issues and Application in Credit Scoring (2008, methodology): “ANN-DT (1999) [ 52 ] P C + R Decision tree induction, similar to TREPAN DecText (2000) [10] P C Decision tree induction STARE (2003) [68] P C Breadth-first search with sampling, prefers categorical variables over continuous variables”
Extracting symbolic rules from trained neural network ensembles (2003, methodology): “[34] grow binary decision trees from trained neural networks with the help of an attribute selection criterion based on significance analysis.”
DeepRED - Rule Extraction from Deep Neural Networks (2016, methodology): “The works dealing with pedagogical rule extraction, for instance, comprise the VIA method discussed in [17, 18], different sampling-based approaches [5, 16, 13, 21, 14], and the RxREN algorithm [2].”