Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning
This work addresses the need for interpretable models in domains like relational learning and rule extraction, offering an incremental improvement by integrating game theory into preference learning.
The authors tackled the problem of interpretable preference learning by framing it as a two-player zero-sum game, proposing an algorithm that incrementally adds features to achieve state-of-the-art accuracy while improving interpretability and feature selection quality.
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.