Fuzzy Semi-supervised support vector machines

Abstract

In this paper, a fuzzy semi-supervised support vector machines (FSS-SVM) algorithm is proposed. It tries to overcome the need for a large labelled training set to learn accurate classifiers. For this, it uses both labelled and unlabelled data for training. It also modulates the effect of the unlabelled data in the learning process. Empirical evaluations showed that by additionally using unlabelled data, FSS-SVM requires less labelled training data than its supervised version, support vector machines, to achieve the same level of classification performance. Also, the incorporated fuzzy membership values of the unlabelled training patterns in the learning process have positively influenced the classification performance in comparison with its crisp variant. © 2011 Springer-Verlag.