Search Machine Learning Repository: @inproceedings{icml2014c1_lia14,
    Publisher = {JMLR Workshop and Conference Proceedings},
    Title = {Condensed Filter Tree for Cost-Sensitive Multi-Label Classification},
    Url = {http://jmlr.org/proceedings/papers/v32/lia14.pdf},
    Abstract = {Different real-world applications of multi-label classification often demand different evaluation criteria. We formalize this demand with a general setup, cost-sensitive multi-label classification (CSMLC), which takes the evaluation criteria into account during learning. Nevertheless, most existing algorithms can only focus on optimizing 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 constructing the label powerset. We successfully cope with the difficulty of having exponentially many extended-classes within the powerset for representation, training and prediction by carefully designing the tree structure and focusing on the key nodes. Experimental results across many real-world datasets validate that CFT is competitive with special purpose algorithms on special criteria and reaches better performance on general criteria.},
    Author = {Chun-liang Li and Hsuan-tien Lin},
    Editor = {Tony Jebara and Eric P. Xing},
    Year = {2014},
    Booktitle = {Proceedings of the 31st International Conference on Machine Learning (ICML-14)},
    Pages = {423-431}
   }