  Case-based reasoning is known to play an important role in several legal
settings. In this paper we focus on a recent approach to case-based reasoning,
supported by an instantiation of abstract argumentation whereby arguments
represent cases and attack between arguments results from outcome disagreement
between cases and a notion of relevance. In this context, relevance is
connected to a form of specificity among cases. We explore how relevance can be
learnt automatically in practice with the help of decision trees, and explore
the combination of case-based reasoning with abstract argumentation (AA-CBR)
and learning of case relevance for prediction in legal settings. Specifically,
we show that, for two legal datasets, AA-CBR and decision-tree-based learning
of case relevance perform competitively in comparison with decision trees. We
also show that AA-CBR with decision-tree-based learning of case relevance
results in a more compact representation than their decision tree counterparts,
which could be beneficial for obtaining cognitively tractable explanations.
