{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/interpretable-preference-learning-a-game","title":"Interpretable preference learning: a game theoretic framework for large margin on-line feature and rule learning","arxiv_id":"1812.07895","date":"2018-12-19","proceeding":null,"authors":["Mirko Polato","Fabio Aiolli"],"abstract":"A large body of research is currently investigating on the connection between\nmachine learning and game theory. In this work, game theory notions are\ninjected into a preference learning framework. Specifically, a preference\nlearning problem is seen as a two-players zero-sum game. An algorithm is\nproposed to incrementally include new useful features into the hypothesis. This\ncan be particularly important when dealing with a very large number of\npotential features like, for instance, in relational learning and rule\nextraction. A game theoretical analysis is used to demonstrate the convergence\nof the algorithm. Furthermore, leveraging on the natural analogy between\nfeatures and rules, the resulting models can be easily interpreted by humans.\nAn extensive set of experiments on classification tasks shows the effectiveness\nof the proposed method in terms of interpretability and feature selection\nquality, with accuracy at the state-of-the-art.","url_abs":"http://arxiv.org/abs/1812.07895v1","url_pdf":"http://arxiv.org/pdf/1812.07895v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"interpretable-preference-learning-a-game","repo_url":"https://github.com/makgyver/PRL","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"relational-reasoning","task_name":"Relational Reasoning"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}