2015 DiscoveringandExploitingDetermi

• (Papagiannopoulou et al., 2015) ⇒ Christina Papagiannopoulou, Grigorios Tsoumakas, and Ioannis Tsamardinos. (2015). “Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783302

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Discovering+and+Exploiting+Deterministic+Label+Relationships+in+Multi-Label+Learning
• http://dl.acm.org/citation.cfm?id=2783258.2783302&preflayout=flat#citedby

Quotes

Author Keywords

• Bayesian network; data mining; deterministic label relationships; knowledge discovery; label constraints; learning; multi-label learning

Abstract

This work presents a 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 entailment: 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 as a deterministic Bayesian network. Marginal probabilities are entered as soft evidence in the network and through probabilistic inference become consistent with the discovered knowledge. Our approach offers robust improvements in mean average precision compared to the standard binary relevance approach across all 12 datasets involved in our experiments. The discovery process helps interesting implicit knowledge to emerge, which could be useful in itself.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 DiscoveringandExploitingDetermi	Christina Papagiannopoulou Grigorios Tsoumakas Ioannis Tsamardinos			Discovering and Exploiting Deterministic Label Relationships in Multi-Label Learning				10.1145/2783258.2783302		2015