Total Margin Based Balanced Relative Margin Machine

Abstract

Inspired by the total margin algorithm, we extend balanced relative margin machine (BRMM) by introducing surplus variables, and propose a total margin based balanced relative (TM-BRMM). TMBRMM not only solves the loss of information points involved, but also addresses outliers at the outer boundaries that limit the maximum distance from points to separating hyperplane. Furthermore, by means of kernel function, it is easy to solve nonlinear separable datasets. The experiments on UCI datasets verify the feasibility and superiority of TM-BRMM.