Least squares twin bounded support vector machines based on L1-norm distance metric for classification

摘要

Highlights?We have enhanced TBSVM to LSTBSVM in least squares sense, while in LSTBSVM the distance is measured by L1-norm.?L1-LSTBSVM has more robustness to outliers, can lower the computational costs and improve the classification performance.?We design a valid iterative algorithm to solve the L1-norm optimal problems, which is an important theoretical contribution.?The method which we proposed can be conveniently extended to solve other improved methods of TWSVM.AbstractIn this paper, we construct a least squares version of the recently proposed twin bounded support vector machine (TBSVM) for binary classification. As a valid classification tool, TBSVM attempts to seek two non-parallel planes that can be produced by solving a pair of quadratic programming problems (QPPs), but this is time-consuming. Here, we solve two systems of linear equations rather t