Pairwise Difference Learning for Classification

Belaid, Mohamed Karim ; Rabus, Maximilian und Hüllermeier, Eyke (28. January 2025): Pairwise Difference Learning for Classification. 27th International Conference on Discovery Science, DS 2024, Pisa, Italy, October 14–16, 2024. Monreale, Anna; Guidotti, Riccardo und Naretto, Francesca (eds.) : In: Discovery Science, Vol. 15244 Springer Nature Switzerland. pp. 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.

• Item Type: Conference or Workshop Item (Paper)
• Form of publication: Publisher's Version
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 004 Data processing computer science
• ISBN: 978-3-031-78980-9
• ISSN: 0302-9743
• Language: English