Semisupervised learning (SSL) models are useful for undertaking classification problems with a small set of labeled samples and a large number of unlabeled samples. In this regard, the family of fuzzy min–max (FMM) neural networks offers the capability of online learning for addressing both unsupervised and supervised problems. As such, this paper proposes a novel two-stage SSL model based on FMM networks, denoted SSL–FMM. The first stage employs the unlabeled samples to generate a number of hyperboxes using the unsupervised FMM algorithm, while the second stage uses the labeled samples to associate the generated hyperboxes with their target classes using the supervised FMM algorithm. In addition, a neighborhood-labeling mechanism based on the Euclidean distance and hyperbox centroids is formulated to associate the unlabeled hyperboxes with the most likely target classes. A number of benchmark problems and a real-world case study are employed to evaluate the effectiveness of the proposed SSL–FMM model. The outcome indicates that SSL–FMM is able to use unlabeled samples effectively and improve the FMM performance, producing promising results compared with other SSL methods in the literature.

半监督学习 (SSL) 模型可用于解决具有一小组标记样本和大量未标记样本的分类问题。在这方面，模糊最小-最大 (FMM) 神经网络系列提供了在线学习的能力，可以解决无监督和有监督的问题。因此，本文提出了一种基于 FMM 网络的新型两阶段 SSL 模型，表示为 SSL-FMM。第一阶段使用未标记的样本使用无监督的 FMM 算法生成多个超框，而第二阶段使用标记的样本使用监督的 FMM 算法将生成的超框与其目标类相关联。此外，基于欧几里德距离和超框质心的邻域标记机制被制定以将未标记的超框与最可能的目标类别相关联。许多基准问题和实际案例研究被用来评估所提出的 SSL-FMM 模型的有效性。结果表明 SSL-FMM 能够有效地使用未标记的样本并提高 FMM 的性能，与文献中的其他 SSL 方法相比，产生了有希望的结果。