Discovering and Exploiting Entailment Relationships in Multi-Label Learning
This work addresses the challenge of enhancing prediction accuracy in multi-label classification for applications where label relationships are implicit, though it is incremental as it builds on existing probabilistic methods.
The paper tackles the problem of improving multi-label learning by enforcing probabilistic adherence to automatically discovered entailment and exclusion relationships among labels, resulting in robust improvements in mean average precision across 12 datasets compared to the standard binary relevance approach.
This work presents a sound probabilistic method for enforcing adherence of the marginal probabilities of a multi-label model to automatically discovered deterministic relationships among labels. In particular we focus on discovering two kinds of relationships among the labels. The first one concerns pairwise positive entailement: pairs of labels, where the presence of one implies the presence of the other in all instances of a dataset. The second concerns exclusion: sets of labels that do not coexist in the same instances of the dataset. These relationships are represented with a Bayesian network. Marginal probabilities are entered as soft evidence in the network and adjusted through probabilistic inference. Our approach offers robust improvements in mean average precision compared to the standard binary relavance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.