The One-versus-One (OvO) strategy is an approach of multi-classification models which focuses on training binary classifiers between each pair of classes. While the OvO strategy takes advantage of balanced training data, the classification accuracy is usually hindered by the voting mechanism to combine all binary classifiers. In this paper, a novel OvO multi-classification model incorporating a joint probability measure is proposed under the deep learning framework. In the proposed model, a two-stage algorithm is developed to estimate the class probability from the pairwise binary classifiers. Given the binary classifiers, the pairwise probability estimate is calibrated by a distance measure on the separating feature hyperplane. From that, the class probability of the subject is estimated by solving a joint probability-based distance minimization problem. Numerical experiments in different applications show that the proposed model achieves generally higher classification accuracy than other state-of-the-art models.

翻译：一对一策略是一种多分类模型方法，其核心在于为每对类别训练一个二分类器。尽管该策略具有训练数据平衡的优势，但将所有二分类器进行投票组合的机制通常限制了分类精度。本文提出一种新型的基于深度学习框架的一对一多分类模型，该模型融合了联合概率度量。在所提出的模型中，开发了一个两阶段算法，用于从成对二分类器中估计类别概率。基于训练好的二分类器，通过分离特征超平面上的距离度量对成对概率估计进行校准。在此基础上，通过求解基于联合概率的距离最小化问题来估计样本的类别概率。不同应用场景下的数值实验表明，本文所提出的模型在分类精度上普遍优于其他现有先进模型。