Dependent binary relevance models for multi-label classification
We propose DBR as a multi-label classifier that exploits conditional label dependence. DBR combines properties of both, chaining and stacking learning strategies. We provide a careful analysis of the relationship between these techniques. We study the underlying dependency structure and the type of training data used. Our experiments show the good performance of DBR in terms of several measures.
multi-label classification label dependence chaining stacking