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Xu, Suping; Yang, Xibei; Yu, Hualong; Yu, Dong-Jun; Yang, Jingyu; Tsang, Eric C.C.
Knowledge-based systems, 07/2016, Letnik: 104Journal Article
•We propose two multi-label learning approaches with LIFT reduction.•The idea of fuzzy rough set attribute reduction is adopted in our approaches.•Sample selection improves the efficiency in feature dimension reduction. In multi-label learning, since different labels may have some distinct characteristics of their own, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches.
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