期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 28, 期 10, 页码 2665-2680出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2016.2581161
关键词
Multi-label classification; label dependency; label transfer; tree classifier; ensemble methods
类别
资金
- Guangzhou Key Laboratory of Robotics and Intelligent Software [15180007]
- National Natural Science Foundation of China (NSFC) [61005061]
- Guangdong Natural Science Foundation [2016A030313479]
- Fundamental Research Funds for the Central Universities [D215048w, 2015ZZ029]
- HKRGC CRF [C1007-15GF]
- HKRGC GRF HKBU [12302715]
Multi-label classification deals with the problem where each example is associated with multiple class labels. Since the labels are often dependent to other labels, exploiting label dependencies can significantly improve the multi-label classification performance. The label dependency in existing studies is often given as prior knowledge or learned from the labels only. However, in many real applications, such prior knowledge may not be available, or labeled information might be very limited. In this paper, we propose a new algorithm, called ML-FOREST, to learn an ensemble of hierarchical multi-label classifier trees to reveal the intrinsic label dependencies. In ML-FOREST, we construct a set of hierarchical trees, and develop a label transfer mechanism to identify the multiple relevant labels in a hierarchical way. In general, the relevant labels at higher levels of the trees capture more discriminable label concepts, and they will be transferred into lower level children nodes that are harder to discriminate. The relevant labels in the hierarchy are then aggregated to compute label dependency and make the final prediction. Our empirical study shows encouraging results of the proposed algorithm in comparison with the state-of-the-art multi-label classification algorithms under Friedman test and post-hoc Nemenyi test.
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