Multi-label classification is an advantageous technique for managing uncertainty in classification problems where each data instance is associated with several labels simultaneously. Such situations are frequent in real-world scenarios, where decisions rely on imprecise or noisy data and adaptableclassification methods are preferred. However, the problem of class imbalance represents a common characteristic of several multi-label datasets, in which the distribution of samples and their corresponding labels is non-uniform across the data space. In this paper, we propose a multi-label classification approach utilizing fuzzy logic in order to deal with the class imbalance problem. To eliminate the need for an expert to determine the logical rules of inference, deep neural networks are adopted, which have proven to be exceptionally effective for such problems. By combining both fuzzy inference systems and deep neural networks, the strengths and weaknesses of each approach can be mitigated. As a further development, a symbolic representation of time series is put in place to reduce data dimensionality and speed up the training procedure. This allows for more flexibility in model application, in particular with respect to time constraints arising from the causality of observed time series. Tests carried out on a multi-label classification dataset related to the current and voltage profiles of several household appliances show that the proposed model outperforms four baseline models for time series classification.

中文翻译：

---

通过模糊深度神经网络进行不平衡类别的多标签分类

多标签分类是一种用于管理分类问题中的不确定性的有利技术，其中每个数据实例同时与多个标签相关联。这种情况在现实场景中很常见，其中决策依赖于不精确或嘈杂的数据，并且首选适应性分类方法。然而，类不平衡问题代表了多个多标签数据集的共同特征，其中样本及其相应标签的分布在数据空间中是不均匀的。在本文中，我们提出了一种利用模糊逻辑的多标签分类方法来处理类别不平衡问题。为了消除专家确定推理逻辑规则的需要，采用了深度神经网络，事实证明这种网络对于解决此类问题非常有效。通过结合模糊推理系统和深度神经网络，可以减轻每种方法的优点和缺点。作为进一步的发展，时间序列的符号表示被到位以减少数据维度并加速训练过程。这使得模型应用具有更大的灵活性，特别是在观察到的时间序列的因果关系所产生的时间限制方面。在与多种家用电器的电流和电压曲线相关的多标签分类数据集上进行的测试表明，所提出的模型优于时间序列分类的四个基线模型。