Safe intuitionistic fuzzy twin support vector machine for semi-supervised learning

Abstract

Learning unlabeled samples without deteriorating performance is a challenge in semi-supervised learning. In this paper, we propose a safe intuitionistic fuzzy twin support vector machine (SIFTSVM) for semi-supervised learning. In our SIFTSVM, whether an unlabeled sample should be learned by a twin support vector machine is determined by its plane intuitionistic fuzzy number. The unlabeled samples are learned gradually according to the current decision environment, which is safer and more precise than learning all of the unlabeled samples simultaneously. Interestingly, the iterative algorithm of our SIFTSVM obtains a solution to a mixed integer programming problem whose global solution corresponds to a classifier by learning the unlabeled samples with implicit labels. Experimental results on several synthetic datasets confirm the safety of our SIFTSVM for learning unlabeled samples, and the results on 56 groups of benchmark datasets demonstrate that our SIFTSVM outperforms the state-of-the-art semi-supervised classifiers on most groups.