Fuzzy least squares twin support vector machines

Abstract

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. In many real-world applications, samples may not deterministically be assigned to a single class; they come naturally with their associated uncertainties Also, samples may not be equally important and their importance degrees affect the classification. Despite its efficiency, LST-SVM still lacks the ability to deal with these situations. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to cope with these difficulties. Two models are introduced for linear FLST-SVM: the first model builds up crisp hyperplanes using training samples and their corresponding membership degrees. The second model, on the other hand, constructs fuzzy hyperplanes using training samples and their membership degrees. We also extend the non-linear FLST-SVM using kernel generated surfaces. Numerical evaluation of the proposed method with synthetic and real datasets demonstrate significant improvement in the classification accuracy of FLST-SVM when compared to well-known existing versions of SVM