arXiv subjects

Tomoya Tate

Publications and source records attributed to Tomoya Tate.

2 recordsLinked to original sources

arXiv2026

Binary Classification from Coupled Pairwise Labels

Even when it is difficult to assign absolute class labels to individual instances, relational information may still be available, such as whether two instances belong to the same class or which instance is more likely to belong to the positive class. In this study, we refer to these two types of information as Similarity/Dissimilarity (SD) labels and Pairwise Comparison (Pcomp) labels, respectively, and consider binary classification that uses both types of relational information from the same instance pairs. SD learning uses the distinction between similar and dissimilar pairs but does not use the ordering within each pair, whereas Pcomp learning uses the ordering within each pair but does not distinguish between similar and dissimilar pairs. We therefore propose SD-Pcomp learning, whose objective function simultaneously preserves the structures of both SD learning and Pcomp learning. The proposed objective function admits two decompositions: one consists of an SD estimator plus a term that represents ordering information from Pcomp labels, and the other consists of a Pcomp estimator plus a term that represents pair-type information from SD labels. These decompositions clarify how the complementary information provided by SD and Pcomp labels is integrated into the proposed objective function. Experiments on eight datasets compare the proposed method with SD learning, Pcomp learning, and a method that takes a convex combination of their objective functions. We evaluate the effect of using both types of relational information on classification performance in terms of classification accuracy and AUC.

cs.LG↗

arXiv2026

Learning from Similarity-Confidence and Confidence-Difference

In practical machine learning applications, it is often challenging to assign accurate labels to data, and increasing the number of labeled instances is often limited. In such cases, Weakly Supervised Learning (WSL), which enables training with incomplete or imprecise supervision, provides a practical and effective solution. However, most existing WSL methods focus on leveraging a single type of weak supervision. In this paper, we propose a novel WSL framework that leverages complementary weak supervision signals from multiple relational perspectives, which can be especially valuable when labeled data is limited. Specifically, we introduce SconfConfDiff Classification, a method that integrates two distinct forms of weaklabels: similarity-confidence and confidence-difference, which are assigned to unlabeled data pairs. To implement this method, we derive two types of unbiased risk estimators for classification: one based on a convex combination of existing estimators, and another newly designed by modeling the interaction between two weak labels. We prove that both estimators achieve optimal convergence rates with respect to estimation error bounds. Furthermore, we introduce a risk correction approach to mitigate overfitting caused by negative empirical risk, and provide theoretical analysis on the robustness of the proposed method against inaccurate class prior probability and label noise. Experimental results demonstrate that the proposed method consistently outperforms existing baselines across a variety of settings.

cs.LG↗