Fuzzy Least Squares Twin Support Vector Machines

TL;DR

This paper introduces Fuzzy LST-SVM, an extension of Least Squares Twin Support Vector Machines that incorporates fuzzy logic to handle uncertain labels and varying sample importance, improving classification accuracy.

Contribution

It proposes two models of FLST-SVM that integrate fuzzy membership degrees into hyperplane construction, addressing real-world data uncertainties.

Findings

Significant accuracy improvement over existing SVM variants.

Effective handling of uncertain labels and importance degrees.

Validated on synthetic and real datasets.

Abstract

Least Squares Twin Support Vector Machine (LST-SVM) has been shown to be an efficient and fast algorithm for binary classification. It combines the operating principles of Least Squares SVM (LS-SVM) and Twin SVM (T-SVM); it constructs two non-parallel hyperplanes (as in T-SVM) by solving two systems of linear equations (as in LS-SVM). Despite its efficiency, LST-SVM is still unable to cope with two features of real-world problems. First, in many real-world applications, labels of samples are not deterministic; they come naturally with their associated membership degrees. Second, samples in real-world applications may not be equally important and their importance degrees affect the classification. In this paper, we propose Fuzzy LST-SVM (FLST-SVM) to deal with these two characteristics of real-world data. Two models are introduced for 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. 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.