A pruning method of refining recursive reduced least squares support vector regression

In this paper, a pruning method is proposed to refine the recursive reduced least squares support vector regression (RRLSSVR) and its improved version (IRRLSSVR), and thus two novel algorithms PruRRLSSVR and PruIRRLSSVR are yielded. This pruning method ranks support vectors by defining a contribution function to the objective function, and then the support vector with the least contribution is pruned unless it is the most recently selected support vector. Consequently, PruRRLSSVR and PruIRRLSSVR outperform RRLSSVR and IRRLSSVR respectively in terms of the number of support vectors while not impairing the generalization performance. In addition, a speedup scheme is presented that reduces the computational burden of computing the contribution function. To show the effectiveness and feasibility of the proposed PruRRLSSVR and PruIRRLSSVR, experiments are performed on ten benchmark data sets and a gas furnace instance.