  A large body of research is currently investigating on the connection between
machine learning and game theory. In this work, game theory notions are
injected into a preference learning framework. Specifically, a preference
learning problem is seen as a two-players zero-sum game. An algorithm is
proposed to incrementally include new useful features into the hypothesis. This
can be particularly important when dealing with a very large number of
potential features like, for instance, in relational learning and rule
extraction. A game theoretical analysis is used to demonstrate the convergence
of the algorithm. Furthermore, leveraging on the natural analogy between
features and rules, the resulting models can be easily interpreted by humans.
An extensive set of experiments on classification tasks shows the effectiveness
of the proposed method in terms of interpretability and feature selection
quality, with accuracy at the state-of-the-art.
