Most semi-supervised learning (SSL) models entail complex structures and iterative training processes as well as face difficulties in interpreting their predictions to users. To address these issues, this paper proposes a new interpretable SSL model using the supervised and unsupervised Adaptive Resonance Theory (ART) family of networks, which is denoted as SSL-ART. Firstly, SSL-ART adopts an unsupervised fuzzy ART network to create a number of prototype nodes using unlabeled samples. Then, it leverages a supervised fuzzy ARTMAP structure to map the established prototype nodes to the target classes using labeled samples. Specifically, a one-to-many (OtM) mapping scheme is devised to associate a prototype node with more than one class label. The main advantages of SSL-ART include the capability of: (i) performing online learning, (ii) reducing the number of redundant prototype nodes through the OtM mapping scheme and minimizing the effects of noisy samples, and (iii) providing an explanation facility for users to interpret the predicted outcomes. In addition, a weighted voting strategy is introduced to form an ensemble SSL-ART model, which is denoted as WESSL-ART. Every ensemble member, i.e., SSL-ART, assigns {\color{black}a different weight} to each class based on its performance pertaining to the corresponding class. The aim is to mitigate the effects of training data sequences on all SSL-ART members and improve the overall performance of WESSL-ART. The experimental results on eighteen benchmark data sets, three artificially generated data sets, and a real-world case study indicate the benefits of the proposed SSL-ART and WESSL-ART models for tackling pattern classification problems.

翻译：大多数半监督学习模型结构复杂且需要迭代训练过程，同时难以向用户解释其预测结果。为解决这些问题，本文提出一种基于监督与无监督自适应共振理论网络的新型可解释半监督学习模型，命名为SSL-ART。首先，SSL-ART采用无监督模糊ART网络利用未标记样本生成若干原型节点；随后，通过监督模糊ARTMAP结构利用标记样本将已建立的原型节点映射至目标类别。具体而言，本文设计了一种一对多映射方案，使得单个原型节点能够关联多个类别标签。SSL-ART的主要优势包括：(i)支持在线学习；(ii)通过OtM映射方案减少冗余原型节点数量，并最小化噪声样本的影响；(iii)为用户提供解释预测结果的可解释性工具。此外，本文引入加权投票策略构建集成SSL-ART模型，命名为WESSL-ART。每个集成成员（即SSL-ART）根据其对各类别的分类性能，为每个类别分配不同的权重，旨在降低训练数据序列对所有SSL-ART成员的影响，并提升WESSL-ART的整体性能。在18个基准数据集、3个人工生成数据集及一项真实案例研究上的实验结果表明，所提出的SSL-ART与WESSL-ART模型在处理模式分类问题方面具有显著优势。