On Label Dependence in Multi-Label Classification

The aim of this paper is to elaborate on the important issue of label dependence in multi-label classification (MLC). Looking at the problem from a statistical perspective, we claim that two different types of label dependence should be distinguished, namely conditional and unconditional. We formally explain the differences and connections between both types of dependence and illustrate them by means of simple examples. Moreover, we given an overview of state-of-the-art algorithms for MLC and categorize them according to the type of label dependence they seek to capture.