Condensed Filter Tree for Cost-Sensitive Multi-Label Classification
Chunliang Li, Hsuan-Tien Lin · 2014
Different real-world applications of multi-label classification often demand different evaluation criteria. We formalize this demand with a gen-eral setup, cost-sensitive multi-label classifica-tion (CSMLC), which takes the evaluation crite-ria into account during learning. Nevertheless, most existing algorithms can only focus on op-timizing a few specific evaluation criteria, and cannot systematically deal with different ones. In this paper, we propose a novel algorithm, called condensed filter tree (CFT), for optimizing any criteria in CSMLC. CFT is derived from reducing CSMLC to the famous filter tree algorithm for cost-sensitive multi-class classification via con-structing the label powerset. We successfully cope with the difficulty of having exponentially many extended-classes within the powerset for representation, training and prediction by care-fully designing the tree structure and focusing on the key nodes. Experimental results across many real-world datasets validate that CFT is compet-itive with special purpose algorithms on special criteria and reaches better performance on gen-eral criteria. 1.