成对差异学习（PDL）如何扩展到分类任务

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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.

Comments:	v2: corrected the e-mail address of E. Hüllermeier and the ORCID of M.K. Belaid; added a reference to the published version. Preprint (version submitted before peer review) of the paper published in Discovery Science (DS 2024), LNCS vol. 15244, Springer
Subjects:	Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2406.20031 [cs.LG]
	(or arXiv:2406.20031v2 [cs.LG] for this version)
	https://doi.org/10.48550/arXiv.2406.20031 arXiv-issued DOI via DataCite
Journal reference:	Discovery Science (DS 2024), Lecture Notes in Computer Science, vol. 15244, pp. 284-299, Springer (2025)
Related DOI:	https://doi.org/10.1007/978-3-031-78980-9_18 DOI(s) linking to related resources

Submission history

From: Mohamed Karim Belaid [view email]
[v1] Fri, 28 Jun 2024 16:20:22 UTC (4,782 KB)
[v2] Thu, 15 Oct 2026 10:26:53 UTC (2,481 KB)