Multi-label learning with label-specific feature reduction

• 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.