Multilabel learning tackles the problem of associating a sample with multiple class labels. This work proposes a new ensemble method for managing multilabel classification: the core of the proposed approach combines a set of gated recurrent units and temporal convolutional neural networks trained with variants of the Adam optimization approach. Multiple Adam variants, including novel one proposed here, are compared and tested; these variants are based on the difference between present and past gradients, with step size adjusted for each parameter. The proposed neural network approach is also combined with Incorporating Multiple Clustering Centers (IMCC), which further boosts classification performance. Multiple experiments on nine data sets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be available at https://github.com/LorisNanni.

翻译：多标签学习解决了将样本与多类标签挂钩的问题。 这项工作提出了管理多标签分类的新的混合方法: 提议方法的核心结合了一组封闭式经常性元件和经亚当优化法变量培训的时发神经网络。 多亚当变体,包括此处提议的新变体,进行了比较和测试; 这些变体基于目前和过去梯度的差异,并按每个参数调整了步数大小。 拟议的神经网络方法还结合了包含多个组合中心(IMCC),这进一步提升了分类性能。 代表多种多标签任务的九套数据集的多项实验显示了我们最佳组合体的稳健性,显示它超越了艺术的状态。 用于生成实验部分中最佳组合体的 MATLAB 代码将在 https://github.com/LorisNani 上公布。