期刊
MACHINE LEARNING
卷 108, 期 8-9, 页码 1193-1230出版社
SPRINGER
DOI: 10.1007/s10994-018-5773-6
关键词
Multi-label classification; Cost-sensitive; Label space dimension reduction
We study multi-label classification (MLC) with three important real-world issues: online updating, label space dimension reduction (LSDR), and cost-sensitivity. Current MLC algorithms have not been designed to address these three issues simultaneously. In this paper, we propose a novel algorithm, cost-sensitive dynamic principal projection (CS-DPP) that resolves all three issues. The foundation of CS-DPP is an online LSDR framework derived from a leading LSDR algorithm. In particular, CS-DPP is equipped with an efficient online dimension reducer motivated by matrix stochastic gradient, and establishes its theoretical backbone when coupled with a carefully-designed online regression learner. In addition, CS-DPP embeds the cost information into label weights to achieve cost-sensitivity along with theoretical guarantees. Experimental results verify that CS-DPP achieves better practical performance than current MLC algorithms across different evaluation criteria, and demonstrate the importance of resolving the three issues simultaneously.
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