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.

Multi-label learning deals with the problem that each instance is associated with multiple labels simultaneously. Due to its ability to cope with the real-world objects with multiple semantic meanings, multi-label learning has been successfully applied in various application domains [1], such as tag recommendation [2, 3], bioinformatics [4, 5, 6], information retrieval [7, 8], rule mining [9, 10], web mining [11, 12], and so on. Formally speaking, suppose the given multi-label data set is denoted by D {x i, y i } n i 1 where x i R d is a feature vector with d dimensions (features) and y i { 1, 1} q is the corresponding label vector with the size of label space being q. Here, y ij 1 indicates that the i-th instance x i has the j-th label (or equivalently, the j-th label is a relevant label of x i), otherwise the j-th label is an irrelevant label of x i . Let X R d be the d-dimensional feature space, and Y { 1, 1} q be the q-dimensional label space, multi-label learning aims to induce a mapping function f: X Y, which is able to correctly predict the label vector of unseen instances. To solve the multi-label learning problem, the most straightforward solution is Binary Relevance (BR) [13, 14], which aims to decompose the original learning problem into a set of independent binary classification problems. However, this solution generally achieves mediocre performance, as label correlations are regrettably ignored. To ease this problem, a large number of multi-label learning approaches take into account label correlations explicitly or implicitly to improve the learning performance.

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Rest of Europe 8.4 Asia Pacific 8.4.1