2013 MultiLabelClassificationbyMinin

• (Kong et al., 2013) ⇒ Xiangnan Kong, Bokai Cao, and Philip S. Yu. (2013). “Multi-label Classification by Mining Label and Instance Correlations from Heterogeneous Information Networks.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487577

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Multi-label+Classification+by+Mining+Label+and+Instance+Correlations+from+Heterogeneous+Information+Networks
• http://dl.acm.org/citation.cfm?id=2487575.2487577&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; data mining; heterogeneous information network; label correlation; multi-label classification

Abstract

Multi-label classification is prevalent in many real-world applications, where each example can be associated with a set of multiple labels simultaneously. The key challenge of multi-label classification comes from the large space of all possible label sets, which is exponential to the number of candidate labels. Most previous work focuses on exploiting correlations among different labels to facilitate the learning process. It is usually assumed that the label correlations are given beforehand or can be derived directly from data samples by counting their label co-occurrences. However, in many real-world multi-label classification tasks, the label correlations are not given and can be hard to learn directly from data samples within a moderate-sized training set. Heterogeneous information networks can provide abundant knowledge about relationships among different types of entities including data samples and class labels. In this paper, we propose to use heterogeneous information networks to facilitate the multi-label classification process. By mining the linkage structure of heterogeneous information networks, multiple types of relationships among different class labels and data samples can be extracted. Then we can use these relationships to effectively infer the correlations among different class labels in general, as well as the dependencies among the label sets of data examples inter-connected in the network. Empirical studies on real-world tasks demonstrate that the performance of multi-label classification can be effectively boosted using heterogeneous information networks.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 MultiLabelClassificationbyMinin	Philip S. Yu Xiangnan Kong Bokai Cao			Multi-label Classification by Mining Label and Instance Correlations from Heterogeneous Information Networks				10.1145/2487575.2487577		2013