Pairwise Difference Learning for Classification

Belaid, Mohamed Karim ; Rabus, Maximilian und Hüllermeier, Eyke (28. Januar 2025): Pairwise Difference Learning for Classification. 27th International Conference, DS 2024, Pisa, Italy, October 14–16, 2024. Monreale, Anna; Guidotti, Riccardo und Naretto, Francesca (Hrsg.): In: Discovery Science, Bd. 15244 Springer Nature Switzerland. S. 284-299

Abstract

Pairwise difference learning (PDL) has recently been introduced as a new meta-learning technique for regression. Instead of learning a mapping from instances to outcomes in the standard way, the key idea is to learn a function that takes two instances as input and predicts the difference between the respective outcomes. Given a function of this kind, predictions for a query instance are derived from every training example and then averaged. This paper extends PDL toward the task of classification and proposes a meta-learning technique for inducing a PDL classifier by solving a suitably defined (binary) classification problem on a paired version of the original training data. We analyze the performance of the PDL classifier in a large-scale empirical study and find that it outperforms state-of-the-art methods in terms of prediction performance. Last but not least, we provide an easy-to-use and publicly available implementation of PDL in a Python package.

• Dokumententyp: Konferenzbeitrag (Paper)
• Publikationsform: Publisher's Version
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-78980-9
• ISSN: 0302-9743
• Sprache: Englisch