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This article is about machine learning: a study of pairwise difference learning, comparing input encoders and pair-selection strategies for classification across roughly 200 datasets. It is primarily an empirical ML methods paper, so it passes the topic gate. Its practical relevance is in the reported findings that restricting training to upper-triangle pairs cuts compute by about 40% with negligible accuracy loss, while kernel-thinning selection adds overhead without benefit. The authors also note that no single configuration dominates, so readers should treat the results as dataset-dependent guidance rather than a universal recipe.
Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection
arXiv:2610.21644v1 Announce Type: cross Abstract: Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances rather than individual samples, effectively inflating the training set from $n$ to $O(n^2)$ examples. However, the PDC framework leaves critical implementation choices underspecified, particularly regarding input representation and pair selection strategies. This work systematically investigates these design decisions through extensive empirical evaluation. We compare four feature encoders (paired features, triplet with relative difference, absolute difference, and a novel triplet with absolute difference) alongside four pair selection strategies ranging from exhaustive to kernel-thinning-based schemes. Experiments across nearly 200 benchmark classification datasets using Decision Tree, Multilayer Perceptron, Gaussian Naive Bayes, and Support Vector Classifier as base models reveal that the proposed triplet with absolute difference encoder ($\phi_{\mathrm{3dabs}}$) achieves the highest average F1 scores. More notably, restricting training to upper-triangle pairs reduces computational time by approximately 40% with negligible accuracy loss. Kernel thinning-based selection provided no accuracy benefits while substantially increasing computational overhead. Overall, PDC configurations outperformed base models in 94% of tested cases, though no single configuration dominated universally, suggesting dataset-specific optimization remains an open challenge.
Abstract:Pairwise Difference Learning for Classification (PDC) enhances supervised learning in low-data regimes by training a binary similarity classifier on pairs of instances rather than individual samples, effectively inflating the training set from $n$ to $O(n^2)$ examples. However, the PDC framework leaves critical implementation choices underspecified, particularly regarding input representation and pair selection strategies. This work systematically investigates these design decisions through extensive empirical evaluation. We compare four feature encoders (paired features, triplet with relative difference, absolute difference, and a novel triplet with absolute difference) alongside four pair selection strategies ranging from exhaustive to kernel-thinning-based schemes. Experiments across nearly 200 benchmark classification datasets using Decision Tree, Multilayer Perceptron, Gaussian Naive Bayes, and Support Vector Classifier as base models reveal that the proposed triplet with absolute difference encoder ($\phi_{\mathrm{3dabs}}$) achieves the highest average F1 scores. More notably, restricting training to upper-triangle pairs reduces computational time by approximately 40% with negligible accuracy loss. Kernel thinning-based selection provided no accuracy benefits while substantially increasing computational overhead. Overall, PDC configurations outperformed base models in 94% of tested cases, though no single configuration dominated universally, suggesting dataset-specific optimization remains an open challenge.
13 pages. Preprint (version submitted before peer review) of the paper published in WorldCIST 2026, LNNS vol. 2092, Springer
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.21644 [cs.LG]
(or arXiv:2610.21644v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.21644 arXiv-issued DOI via DataCite (pending registration)
Recent Trends and Challenges in Information Systems and Technologies (WorldCIST 2026), Lecture Notes in Networks and Systems, vol. 2092, pp. 366-378, Springer (2026)
https://doi.org/10.1007/978-3-032-32023-0_31 DOI(s) linking to related resources
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