Safe instance screening for primal multi-label ProSVM

Abstract

As an important model in multi-label learning, ProSVM considers two problems simultaneously: one is to distinguish the relevant labels from the irrelevant labels of instances, and the other is to rank the relevant labels. Thus it produces better generalization ability in comparison with other multi-label models. However, ProSVM leads to high computational cost when the label space is enormous since it employs the label-pairs instead of the instances in the training process. To effectively deal with this defect, a safe screening rule is proposed to speed up the learning process, named PSSR. Different from the existing safe screening rule, our screening method is constructed based on solving the primal problem of ProSVM rather than the dual one. So it can address large-scale problems. To the best of our knowledge, it is the first safe screening rule for the primal problem in multi-label learning. Experimental results on eight benchmark datasets demonstrate the superiority of PSSR. Finally, our PSSR is applied to the clinical data of diabetic patients, and obtain better performance.