On the Impact of Data Selection when Applying Machine Learning in Abstract Argumentation

Abstract

. We examine the impact of both training and test data selection in ma- chine learning applications for abstract argumentation, in terms of prediction accuracy and generalizability. For that, we first review previous studies from a data- centric perspective and conduct some experiments to back up our analysis. We further present a novel algorithm to generate particularly challenging argumentation frameworks wrt. the task of deciding skeptical acceptability under preferred semantics. Moreover, we investigate graph-theoretical aspects of the existing datasets and perform some experiments which show that some simple properties (such as in-degree and out-degree of an argument) are already quite strong indicators of whether or not an argument is skeptically accepted under preferred semantics.