Multiple birth least squares support vector machine for multi-class classification

摘要

Least squares twin support vector machine (LSTSVM) was initially designed for binary classification. However, practical problems often require the discrimination more than two categories. To tackle multi-class classification problem, a novel algorithm, called multiple birth least squares support vector machine (MBLSSVM), is proposed. Our MBLSSVM solves K quadratic programming problems (QPPs) to obtain K hyperplanes, each problem is similar to binary LSTSVM. Comparison against the Multi-LSTSVM, Multi-TWSVM, MBSVM and our MBLSSVM on both UCI datasets and ORL, YALE face datasets illustrates the effectiveness of the proposed method.