ML-SLSTSVM: a new structural least square twin support vector machine for multi-label learning

Abstract

Multi-label learning (MLL) is a special supervised learning task, where any single instance possibly belongs to several classes simultaneously. Nowadays, MLL methods are increasingly required by modern applications, such as protein function classification, speech recognition and textual data classification. In this paper, a structural least square twin support vector machine (SLSTSVM) classifier for multi-label learning is presented. This proposed ML-SLSTSVM focuses on the cluster-based structural information of the corresponding class in each optimization problem, which is vital for designing a good classifier in different real-world problems. This method is extended to a nonlinear version by the kernel trick. Experimental results demonstrate that proposed method is superior in generalization performance to other classifiers.