arXiv cs.AI· Bernard Georges, Mohamed Karim Belaid, Manuel Rodrigues·· 2 天前AI 评分18
成对差异学习（PDC）编码策略与训练对选择对比研究
Efficient Pairwise Difference Learning: A Comparative Study of Encoding Strategies and Training Pair Selection
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一项针对成对差异学习分类（PDC）的系统实证研究比较了四种特征编码器与四种训练对选择策略，在近 200 个基准分类数据集上以 Decision Tree、Multilayer Perceptron、Gaussian Naive Bayes 和 Support Vector Classifier 为基模型测试。
正文 · 原文
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.
| Comments: | 13 pages. Preprint (version submitted before peer review) of the paper published in WorldCIST 2026, LNNS vol. 2092, Springer |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | 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) |
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| Journal reference: | Recent Trends and Challenges in Information Systems and Technologies (WorldCIST 2026), Lecture Notes in Networks and Systems, vol. 2092, pp. 366-378, Springer (2026) |
| Related DOI: | https://doi.org/10.1007/978-3-032-32023-0_31
DOI(s) linking to related resources |
Submission history
From: Mohamed Karim Belaid [view email]
[v1]
Thu, 15 Oct 2026 10:25:11 UTC (141 KB)
来源：arXiv cs.AI · arxiv.org