The paper evaluates how different encoding strategies and pair selection methods affect Pairwise Difference Learning (PDC) for classification. It compares four encoders—including a new triplet with absolute difference—and four pair selection schemes across almost 200 datasets using several base models. The study finds that the new encoder yields the best average F1 scores, that using only upper‑triangle pairs cuts training time by about 40% with little accuracy loss, and that no single configuration dominates across all datasets.
By Bernard Georges, Mohamed Karim Belaid, Manuel Rodrigues
The paper introduces SD-Pcomp learning, a binary classification framework that jointly utilizes Similarity/Dissimilarity (SD) labels and Pairwise Comparison (Pcomp) labels from instance pairs. It proposes an objective function that can be decomposed into either an SD estimator plus ordering information or a Pcomp estimator plus pair-type information, thereby integrating complementary relational cues. Experiments on eight datasets demonstrate that combining both label types improves classification accuracy and AUC compared to using either alone or a simple convex combination.
By Tomoya Tate, Kosuke Sugiyama, Masato Uchida
arXiv:2511. 19359v2 Announce Type: replace Abstract: Conformal Prediction (CP) has emerged as a powerful statistical framework for high-stakes classification applications.
By Ariel Fargion, Lahav Dabah, Tom Tirer
arXiv:2610.27669v1 Announce Type: cross
Abstract: Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences. Here, we introdu...
By Robert Bain, Marjan Rashidi, Elizabeth Chrastil
arXiv:2303. 18031v2 Announce Type: replace-cross Abstract: In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases.
By Masashi Noguchi, Shinichi Shirakawa
The paper introduces a method for combining heterogeneous, allied datasets—datasets that share the same class labels but have disjoint objects and largely distinct feature spaces—into a single unified feature space. By applying matrix completion to this merged space, the authors create a unified dataset that enables knowledge transfer between the original datasets. Experiments across multiple dataset pairs and classifiers show that models trained on the unified representation consistently outperform those trained separately on each dataset.
By Girish Keshav Palshikar