Accelerating improved twin support vector machine with safe screening rule

Abstract

Improved twin support vector machine (ITSVM) is a binary classification model with strong theoretical interpretation. Compared with Twin bound support vector machine (TWBSVM), it avoids the matrix inverse operation. However, the disadvantage of slow speed is exposed during the training process. Thus authors are motivated to employ safe screening method to reduce the scale of dual ITSVM and accelerate its computational speed. More specifically, the proposed method is to identify redundant points in advance and eliminate them before actually solving the problem. If safe screening method is directly used to accelerate ITSVM, it will be inevitable to bring matrix inverse operation during the screening process. In this case, a screening method which is distinct from existing safe screening method is devised to avoid calculating inverse matrix. Meanwhile an improved dual coordinate descent method (DCDM) is employed to accelerate ITSVM. Experiments on eleven real data sets are conducted to demonstrate the effectiveness of the proposed acceleration method.