Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by
  Answer Set Programming
  We propose a method for generating explainable rule sets from tree-ensemble
learners using Answer Set Programming (ASP). To this end, we adopt a
decompositional approach where the split structures of the base decision trees
are exploited in the construction of rules, which in turn are assessed using
pattern mining methods encoded in ASP to extract interesting rules. We show how
user-defined constraints and preferences can be represented declaratively in
ASP to allow for transparent and flexible rule set generation, and how rules
can be used as explanations to help the user better understand the models.
Experimental evaluation with real-world datasets and popular tree-ensemble
algorithms demonstrates that our approach is applicable to a wide range of
classification tasks.
