  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.
