Multi-Label Classification with Label Constraints

We extend the multi-label classification setting with constraints on labels. This leads to two new machine learning tasks: First, the label constraints must be properly integrated into the classification process to improve its performance and second, we can try to automatically derive useful constraints from data. In this paper, we experiment with two constraint-based correction approaches as post-processing step within the ranking by pairwise comparison (RPC)-framework. In addition, association rule learning is considered for the task of label constraints learning. We report on the current status of our work, together with evaluations on synthetic datasets and two real-world datasets.