# Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning

## TL;DR

This paper introduces a game-theoretic framework for preference learning that incrementally incorporates features, ensuring interpretability and high accuracy, especially useful for large feature sets like in relational learning.

## Contribution

It presents a novel game-theoretic approach for online feature and rule learning, with proven convergence and enhanced interpretability.

## Key findings

- Effective feature selection in large datasets
- Models are easily interpretable by humans
- Achieves state-of-the-art accuracy in classification

## Abstract

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.