Multi-label Learning with Label-Specific Feature Selection

In multi-label learning, an efficient approach with label-specific features named LIFT has been presented, since different labels may have some distinct characteristics. However, the construction of label-specific features by simply assigning equal weight to each instance ignores the relevance among samples, which might increase the dimensionalities and result in a large amount of redundant information. In order to reduce the redundancy, a novel yet effective multi-label learning approach with weighted label-specific feature selection by using information theory (WFSI-LIFT) is proposed. In WFSI-LIFT, we employ the information theory to implement label-specific feature selection and assign different weights to the different class instance according to imbalance rate(IR). And then, comprehensive experiments across 8 real-world multi-label data sets indicate that, WFSI-LIFT can not only reduce the dimensionalities of label-specific features and enhance the performance compared with LIFT, but also validate the superiority of our approach against other well-established multi-label learning algorithms.