Improved Multi-Label Classification Using Inter-Dependence Structure via a Generative Mixture Model
Ramanuja Simha, Hagit Shatkay · Frontiers in artificial intelligence and applications · 2016
Single-label classification associates each instance with a single label, while multi-label classification (MLC), assigns multiple labels to instances. Simple MLC systems assume that labels are independent of one another, while more complex approaches capture inter-dependencies among labels. Experiments comparing performance of MLC systems demonstrate that there is much room for improvement.