Prediction of multi-dimensional labels plays an important role in machine learning problems. We found that the classical binary labels could not reflect the contents and their relationships in an instance. Hence, we propose a multi-label classification model based on interval type-2 fuzzy logic. In the proposed model, we use a deep neural network to predict the type-1 fuzzy membership of an instance and another one to predict the fuzzifiers of the membership to generate interval type-2 fuzzy memberships. We also propose a loss function to measure the similarities between binary labels in datasets and interval type-2 fuzzy memberships generated by our model. The experiments validate that our approach outperforms baselines on multi-label classification benchmarks.

翻译：多维标签预测在机器学习问题中具有重要作用。我们发现经典二元标签无法准确反映实例中的内容及其相互关系。为此，我们提出了一种基于区间二型模糊逻辑的多标签分类模型。在该模型中，我们使用深度神经网络预测实例的一型模糊隶属度，并通过另一个网络预测该隶属度的模糊化因子，从而生成区间二型模糊隶属度。同时，我们设计了一种损失函数，用于衡量数据集中二元标签与模型生成的区间二型模糊隶属度之间的相似性。实验结果表明，我们的方法在多标签分类基准测试中优于基线模型。