2009 EffectiveMultiLabelActiveLearni

• (Yang et al., 2009) ⇒ Bishan Yang, Jian-Tao Sun, Tengjiao Wang, and Zheng Chen. (2009). “Effective Multi-label Active Learning for Text Classification.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557119

Subject Headings:

Notes

• Categories and Subject Descriptors: H.3.3 Information Systems: Information Search and Retrieval; I.5.2 Design Methodology: Classifier Design and Evaluation.
• General Terms: Algorithms, Performance, Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22Effective+multi-label+active+learning+for+text+classification%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557119&preflayout=flat#citedby

Quotes

Author Keywords

Active Learning, Text Classification, Multi-label Classification, Support Vector Machines

Abstract

Labeling text data is quite time-consuming but essential for automatic text classification. Especially, manually creating multiple labels for each document may become impractical when a very large amount of data is needed for training multi-label text classifiers. To minimize the human-labeling efforts, we propose a novel multi-label active learning approach which can reduce the required labeled data without sacrificing the classification accuracy. Traditional active learning algorithms can only handle single-label problems, that is, each data is restricted to have one label. Our approach takes into account the multi-label information, and select the unlabeled data which can lead to the largest reduction of the expected model loss. Specifically, the model loss is approximated by the size of version space, and the reduction rate of the size of version space is optimized with Support Vector Machines (SVM). An effective label prediction method is designed to predict possible labels for each unlabeled data point, and the expected loss for multi-label data is approximated by summing up losses on all labels according to the most confident result of label prediction. Experiments on several real-world data sets (all are publicly available) demonstrate that our approach can obtain promising classification result with much fewer labeled data than state-of-the-art methods.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 EffectiveMultiLabelActiveLearni	Jian-Tao Sun Bishan Yang Tengjiao Wang Zheng Chen			Effective Multi-label Active Learning for Text Classification		KDD-2009 Proceedings		10.1145/1557019.1557119		2009