Sparse elastic net multi-label rank support vector machine with pinball loss and its applications

Abstract

Multi-label rank support vector machine (RankSVM) is an effective technique to deal with multi-label classification problems, which has been widely used in various fields. However, it is sensitive to noise points and cannot delete redundant features for high dimensional problems. Therefore, to address the above two limitations, a sparse elastic net multi-label RankSVM with pinball loss (pin-ENR) is first proposed in this paper. On the one hand, pinball loss is employed to enhance the robustness. On the other hand, it adopts the sparse elastic net regularization, so that it can do variable selection. However, it still has challenges for large-scale problems with a huge number of features, samples, and labels. Therefore, motivated by the sparsity of pin-ENR, a safe simultaneous feature and label-pair elimination rule is further constructed for accelerating pin-ENR, which is termed as FLER-pin-ENR. Its main idea is to delete a large number of inactive features and label-pairs simultaneously before training without sacrificing accuracy. Numerical experiments on four synthetic and seven benchmark datasets demonstrate the feasibility and validity. Moreover, we apply our FLER-pin-ENR to the diagnosis of diabetes complications and the natural scene image classification problems, which further verifies the practicability of our proposed method.