Efficient sparse least squares support vector machines for pattern classification

We propose an efficient sparse least squares support vector machine, named ε-least squares support vector machine (ε-LSSVM), for binary classification. By introducing the ε-insensitive loss function instead of the quadratic loss function into LSSVM, ε-LSSVM has several improved advantages compared with the plain LSSVM: (1) It is actually a kind of ε-support vector regression (ε-SVR), the only difference here is that it takes the binary classification problem as a special kind of regression problem; (2) The plain LSSVM is only its special case with the parameter ε = 0; (3) It has the sparseness which is controlled by the parameter ε; (4) It can be implemented efficiently by SMO for large scale problems. Experimental results on several benchmark data sets show the effectiveness of our method in sparseness and classification accuracy, and therefore confirm the above conclusion further.