2012 MultiLabelHypothesisReuse

• (Huang et al., 2012) ⇒ Sheng-Jun Huang, Yang Yu, and Zhi-Hua Zhou. (2012). “Multi-label Hypothesis Reuse.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339615

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Cited By

• http://scholar.google.com/scholar?q=%222012%22+Multi-label+Hypothesis+Reuse
• http://dl.acm.org/citation.cfm?id=2339530.2339615&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; hypothesis reuse; label relationship; learning; multi-label learning

Abstract

Multi-label learning arises in many real-world tasks where an object is naturally associated with multiple concepts. It is well-accepted that, in order to achieve a good performance, the relationship among labels should be exploited. Most existing approaches require the label relationship as prior knowledge, or exploit by counting the label co-occurrence. In this paper, we propose the MAHR approach, which is able to automatically discover and exploit label relationship. Our basic idea is that, if two labels are related, the hypothesis generated for one label can be helpful for the other label. MAHR implements the idea as a boosting approach with a hypothesis reuse mechanism. In each boosting round, the base learner for a label is generated by not only learning on its own task but also reusing the hypotheses from other labels, and the amount of reuse across labels provides an estimate of the label relationship. Extensive experimental results validate that MAHR is able to achieve superior performance and discover reasonable label relationship. Moreover, we disclose that the label relationship is usually asymmetric.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 MultiLabelHypothesisReuse	Zhi-Hua Zhou Sheng-Jun Huang Yang Yu			Multi-label Hypothesis Reuse				10.1145/2339530.2339615		2012