Towards Explainable MultiLabel Classification Multilabel classification is a very active research area and many realworld applications need efficient multilabel learning. During recent years, explaining machine learning predictions is also a very hot topic. A lot of approaches have been proposed for explaining multiclass classifier predictions. However, almost nothing has been proposed for multilabel and ensemble approaches. This paper brings two main contributions. It first proposes a natural framework consisting in reasoning with base classifier explanations in order to provide explanations for the multilabel predictions. The second contribution focuses on binary relevance, a widely used approach in multilabel classification, and distinguishes two kinds of explanations: common explanations shared by all base classifiers predicting positive labels and joint explanations combining explanations from each base classifier predicting a positive label. The paper proposes an efficient approach for deriving such explanations. Experimental studies show positive results that can be achieved on many multilabel datasets.

Generative Correlation Discovery Network for Multilabel Learning The goal of Multilabel learning is to predict multiple labels of each single instance. This is a challenging problem since the training data is limited, longtail label distribution, and complicated label correlations. Generally, more training samples and label correlation knowledge would benefit the learning performance. However, it is difficult to obtain largescale welllabeled datasets, and building such a label correlation map requires sophisticated semantic knowledge. To this end, we propose an endtoend Generative Correlation Discovery Network (GCDN) method for multilabel learning in this paper. GCDN captures the existing data distribution, and synthesizes diverse data to enlarge the diversity of the training features; meanwhile, it also learns the label correlations based on a specificallydesigned, simple but effective correlation discovery network to automatically discover the label correlations and considerately improve the label prediction accuracy. Extensive experiments on several benchmarks are provided to demonstrate the effectiveness, efficiency, and high accuracy of our approach.